Final exam review guiding questions#
+Final exam preparation: guiding questions#
Imports#
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Lecture 21: Survival analysis
Lecture 22: Communication
Lecture 24: Deployment and conclusion
-Final exam review guiding questions
+Final exam preparation: guiding questions
Appendix A: Demo of feature engineering for text data
Appendix B: Multi-class, meta-strategies
diff --git a/objects.inv b/objects.inv
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Lecture 21: Survival analysis
Lecture 22: Communication
Lecture 24: Deployment and conclusion
-Final exam review guiding questions
+Final exam preparation: guiding questions
Appendix A: Demo of feature engineering for text data
Appendix B: Multi-class, meta-strategies
diff --git a/searchindex.js b/searchindex.js
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+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"": [[33, "id1"]], "(Optional) Changing the data": [[20, "optional-changing-the-data"], [83, "optional-changing-the-data"]], "(Optional) Evaluation": [[29, "optional-evaluation"], [50, "optional-evaluation"], [94, "optional-evaluation"]], "(Optional) Evaluation metrics for multi-class classification": [[20, "optional-evaluation-metrics-for-multi-class-classification"], [83, "optional-evaluation-metrics-for-multi-class-classification"]], "(Optional) Example 1: Optimization bias": [[19, "optional-example-1-optimization-bias"], [82, "optional-example-1-optimization-bias"]], "(Optional) Example 2: Optimization bias": [[19, "optional-example-2-optimization-bias"], [82, "optional-example-2-optimization-bias"]], "(Optional) Fancier methods": [[19, "optional-fancier-methods"], [38, "optional-fancier-methods"], [39, "optional-fancier-methods"], [82, "optional-fancier-methods"]], "(Optional) Fitting in boosted regression trees.": [[22, "optional-fitting-in-boosted-regression-trees"], [42, "optional-fitting-in-boosted-regression-trees"], [85, "optional-fitting-in-boosted-regression-trees"]], "(Optional) Forward or backward selection": [[24, "optional-forward-or-backward-selection"], [87, "optional-forward-or-backward-selection"]], "(Optional) Macro average and weighted average": [[20, "optional-macro-average-and-weighted-average"], [83, "optional-macro-average-and-weighted-average"]], "(Optional) Parametric vs non parametric": [[15, "optional-parametric-vs-non-parametric"], [78, "optional-parametric-vs-non-parametric"]], "(Optional) Passing probability distributions to random search": [[19, "optional-passing-probability-distributions-to-random-search"], [38, "optional-passing-probability-distributions-to-random-search"], [39, "optional-passing-probability-distributions-to-random-search"], [82, "optional-passing-probability-distributions-to-random-search"]], "(Optional) Prediction in boosted regression trees": [[22, "optional-prediction-in-boosted-regression-trees"], [42, "optional-prediction-in-boosted-regression-trees"], [85, "optional-prediction-in-boosted-regression-trees"]], "(Optional) Problems with feature selection": [[24, "optional-problems-with-feature-selection"], [87, "optional-problems-with-feature-selection"]], "(Optional) Search and score": [[24, "optional-search-and-score"], [87, "optional-search-and-score"]], "(Optional) Searching for optimal parameters with successive halving\u00b6": [[19, "optional-searching-for-optimal-parameters-with-successive-halving"], [38, "optional-searching-for-optimal-parameters-with-successive-halving"], [39, "optional-searching-for-optimal-parameters-with-successive-halving"], [82, "optional-searching-for-optimal-parameters-with-successive-halving"]], "(Optional) Setting up a directory structure and environment": [[96, "optional-setting-up-a-directory-structure-and-environment"]], "(Optional) Some more details": [[20, "optional-some-more-details"], [40, "optional-some-more-details"], [83, "optional-some-more-details"]], "(Supervised) machine learning: popular definition": [[12, "supervised-machine-learning-popular-definition"], [75, "supervised-machine-learning-popular-definition"]], "(iClicker) Exercise 14.1": [[24, "id1"], [87, "id1"]], "(iClicker) Exercise 21.1": [[29, "iclicker-exercise-21-1"], [50, "iclicker-exercise-21-1"], [94, "iclicker-exercise-21-1"]], "(iClicker) Exercise 21.2": [[29, "iclicker-exercise-21-2"], [29, "id5"], [50, "iclicker-exercise-21-2"], [94, "iclicker-exercise-21-2"]], "(iClicker) Exercise 3.1": [[15, "iclicker-exercise-3-1"]], "(iClicker) Exercise 3.2": [[15, "iclicker-exercise-3-2"]], "(iClicker) Exercise 4.1": [[78, "iclicker-exercise-4-1"]], "(iClicker) Exercise 4.2": [[78, "iclicker-exercise-4-2"]], "(iClicker) Exercise 5.1": [[16, "iclicker-exercise-5-1"], [79, "iclicker-exercise-5-1"]], "(iClicker) Exercise 5.2": [[16, "iclicker-exercise-5-2"], [79, "iclicker-exercise-5-2"]], "(iClicker) Exercise 5.3": [[16, "iclicker-exercise-5-3"], [79, "iclicker-exercise-5-3"]], "(iClicker) Exercise 6.1": [[17, "iclicker-exercise-6-1"], [80, "iclicker-exercise-6-1"]], "(iClicker) Exercise 6.2": [[17, "iclicker-exercise-6-2"], [80, "iclicker-exercise-6-2"]], "(iClicker) Exercise 7.1": [[18, "iclicker-exercise-7-1"], [81, "iclicker-exercise-7-1"]], "(iClicker) Exercise 7.2": [[18, "iclicker-exercise-7-2"], [81, "iclicker-exercise-7-2"]], "(iClicker) Exercise 8.1": [[19, "iclicker-exercise-8-1"], [82, "iclicker-exercise-8-1"]], "(iClicker) Midterm poll": [[25, "iclicker-midterm-poll"], [88, "iclicker-midterm-poll"]], "15.1 Select all of the following statements which are True (iClicker)": [[25, "select-all-of-the-following-statements-which-are-true-iclicker"], [88, "select-all-of-the-following-statements-which-are-true-iclicker"]], "15.2 Select all of the following statements which are True (iClicker)": [[25, "id1"], [88, "id1"]], "15.3 Select all of the following statements which are True (iClicker)": [[25, "id3"], [88, "id3"]], "16.1 Select all of the following statements which are True (iClicker)": [[26, "select-all-of-the-following-statements-which-are-true-iclicker"], [89, "select-all-of-the-following-statements-which-are-true-iclicker"]], "16.2 Select all of the following statements which are True (iClicker)": [[26, "id2"], [89, "id2"]], "16.3 Select all of the following statements which are True": [[26, "select-all-of-the-following-statements-which-are-true"], [89, "select-all-of-the-following-statements-which-are-true"]], "330 vs. 340": [[96, "vs-340"]], "A few comments on PR curve": [[20, "a-few-comments-on-pr-curve"], [40, "a-few-comments-on-pr-curve"], [83, "a-few-comments-on-pr-curve"]], "A few comments on clustering evaluation": [[26, "a-few-comments-on-clustering-evaluation"], [89, "a-few-comments-on-clustering-evaluation"]], "AP score": [[20, "ap-score"], [40, "ap-score"], [83, "ap-score"]], "AP vs. F1-score": [[20, "ap-vs-f1-score"], [40, "ap-vs-f1-score"], [83, "ap-vs-f1-score"]], "API on the localhost": [[96, "api-on-the-localhost"]], "AUC or AP?": [[20, "auc-or-ap"]], "About this course": [[12, "about-this-course"]], "About this document": [[8, "about-this-document"]], "Academic concessions": [[101, "academic-concessions"]], "Accessing homework assignments": [[7, "accessing-homework-assignments"]], "Accessing learned parameters": [[18, "accessing-learned-parameters"], [81, "accessing-learned-parameters"]], "Activity": [[12, "activity"]], "Activity (~4 mins)": [[43, "activity-4-mins"]], "Activity (~5 mins)": [[23, "activity-5-mins"], [23, "id3"], [86, "activity-5-mins"], [86, "id3"]], "Activity: Context and word meaning": [[48, "activity-context-and-word-meaning"], [91, "activity-context-and-word-meaning"]], "Activity: Discuss the following questions in your group": [[33, "activity-discuss-the-following-questions-in-your-group"], [64, "activity-discuss-the-following-questions-in-your-group"], [64, "id1"], [64, "id2"]], "Activity: How can you measure quality of the data? (~3 mins)": [[24, "activity-how-can-you-measure-quality-of-the-data-3-mins"], [87, "activity-how-can-you-measure-quality-of-the-data-3-mins"]], "Activity: explaining GridSearchCV (15 min)": [[30, "activity-explaining-gridsearchcv-15-min"], [51, "activity-explaining-gridsearchcv-15-min"], [95, "activity-explaining-gridsearchcv-15-min"]], "Adding/removing columns with [] and drop()": [[8, "adding-removing-columns-with-and-drop"]], "Adding/removing rows with [] and drop()": [[8, "adding-removing-rows-with-and-drop"]], "Additional submission instructions": [[7, "additional-submission-instructions"]], "Addressing class imbalance": [[20, "addressing-class-imbalance"], [83, "addressing-class-imbalance"]], "Advantages of RandomizedSearchCV": [[19, "advantages-of-randomizedsearchcv"], [19, "id1"], [38, "advantages-of-randomizedsearchcv"], [38, "id1"], [39, "advantages-of-randomizedsearchcv"], [39, "id1"], [82, "advantages-of-randomizedsearchcv"], [82, "id1"]], "Alternative and more compact 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"approach-1-only-consider-the-examples-where-churn-yes"]], "Approach 2: Assume everyone churns right now": [[29, "approach-2-assume-everyone-churns-right-now"], [50, "approach-2-assume-everyone-churns-right-now"], [94, "approach-2-assume-everyone-churns-right-now"]], "Approach 3: Survival analysis": [[29, "approach-3-survival-analysis"], [50, "approach-3-survival-analysis"], [94, "approach-3-survival-analysis"]], "Approach from all angles": [[30, "approach-from-all-angles"], [51, "approach-from-all-angles"], [95, "approach-from-all-angles"]], "Are we doing better with class_weight=\"balanced\"?": [[20, "are-we-doing-better-with-class-weight-balanced"], [83, "are-we-doing-better-with-class-weight-balanced"]], "Area under the curve (AUC)": [[20, "area-under-the-curve-auc"], [40, "area-under-the-curve-auc"], [83, "area-under-the-curve-auc"]], "Assessing on the test set": [[33, "assessing-on-the-test-set"], [64, "assessing-on-the-test-set"]], "Assignments": [[101, "assignments"]], 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class": [[12, "checklist-for-you-before-next-class"]], "Choosing K [video]": [[25, "choosing-k-video"], [88, "choosing-k-video"]], "Choosing n_neighbors": [[15, "choosing-n-neighbors"], [78, "choosing-n-neighbors"]], "Citing sources": [[7, "citing-sources"]], "Class imbalance in training sets": [[20, "class-imbalance-in-training-sets"], [83, "class-imbalance-in-training-sets"]], "Class meetings": [[101, "class-meetings"]], "Classification report": [[20, "classification-report"], [40, "classification-report"], [83, "classification-report"]], "Classification vs. Regression": [[13, "classification-vs-regression"], [76, "classification-vs-regression"]], "Classification with KNeighborsClassifier": [[34, "classification-with-kneighborsclassifier"]], "Clustering": [[99, "clustering"]], "Clustering Activity (~5 mins)": [[25, "clustering-activity-5-mins"], [88, "clustering-activity-5-mins"]], "Clustering motivation [video]": [[25, "clustering-motivation-video"], [88, "clustering-motivation-video"]], "Clustering: Input and (possible) output": [[25, "clustering-input-and-possible-output"], [88, "clustering-input-and-possible-output"]], "Code of conduct": [[101, "code-of-conduct"]], "Coefficients and intercept": [[18, "coefficients-and-intercept"], [81, "coefficients-and-intercept"]], "ColumnTransformer example": [[17, "columntransformer-example"], [80, "columntransformer-example"]], "ColumnTransformer on the California housing dataset": [[17, "columntransformer-on-the-california-housing-dataset"], [80, "columntransformer-on-the-california-housing-dataset"]], "ColumnTransformer: Transformed data": [[17, "columntransformer-transformed-data"], [80, "columntransformer-transformed-data"]], "Coming up \u2026": [[14, "coming-up"], [77, "coming-up"]], "Coming up:": [[15, "coming-up"], [78, "coming-up"]], "Command-line git": [[5, "command-line-git"]], "Common applications": [[25, "common-applications"], [88, "common-applications"]], "Common preprocessing 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"Creating utility matrix": [[47, "creating-utility-matrix"], [90, "creating-utility-matrix"]], "Credit": [[10, "credit"]], "Cross validation with different metrics": [[20, "cross-validation-with-different-metrics"], [40, "cross-validation-with-different-metrics"], [83, "cross-validation-with-different-metrics"]], "Cross-validation": [[28, "cross-validation"], [28, "id4"], [33, "cross-validation"], [49, "cross-validation"], [49, "id5"], [64, "cross-validation"], [93, "cross-validation"], [93, "id4"]], "Cross-validation [video]": [[14, "cross-validation-video"], [77, "cross-validation-video"]], "Cross-validation to the rescue!!": [[14, "cross-validation-to-the-rescue"], [77, "cross-validation-to-the-rescue"]], "Cross-validation using scikit-learn": [[14, "cross-validation-using-scikit-learn"], [77, "cross-validation-using-scikit-learn"]], "Curse of dimensionality": [[15, "curse-of-dimensionality"], [78, "curse-of-dimensionality"]], "Customer churn": [[29, "customer-churn"], [50, "customer-churn"], [94, "customer-churn"]], "Customer segmentation": [[25, "customer-segmentation"], [88, "customer-segmentation"]], "DBSCAN": [[46, "dbscan"]], "DBSCAN [video]": [[26, "dbscan-video"], [89, "dbscan-video"]], "DBSCAN introduction": [[26, "dbscan-introduction"], [89, "dbscan-introduction"]], "DBSCAN: failure cases": [[26, "dbscan-failure-cases"], [26, "id1"], [89, "dbscan-failure-cases"], [89, "id1"]], "Data": [[17, "data"], [18, "data"], [22, "data"], [23, "data"], [23, "id1"], [42, "data"], [43, "data"], [43, "id1"], [80, "data"], [81, "data"], [85, "data"], [86, "data"], [86, "id1"]], "Data Splitting [video]": [[14, "data-splitting-video"], [77, "data-splitting-video"]], "Data and main approaches": [[47, "data-and-main-approaches"], [90, "data-and-main-approaches"]], "Data and splitting": [[35, "data-and-splitting"]], "Data exploration": [[25, "data-exploration"], [88, "data-exploration"]], "Data splitting": [[33, "data-splitting"], [64, "data-splitting"]], "Dataframe summaries": [[8, "dataframe-summaries"]], "Dataset": [[27, "dataset"], [30, "dataset"], [51, "dataset"], [92, "dataset"], [95, "dataset"]], "Dataset [video]": [[21, "dataset-video"], [41, "dataset-video"], [84, "dataset-video"]], "Dataset for demonstration": [[20, "dataset-for-demonstration"], [40, "dataset-for-demonstration"], [83, "dataset-for-demonstration"]], "Dataset, splitting, and baseline": [[16, "dataset-splitting-and-baseline"], [79, "dataset-splitting-and-baseline"]], "Datasets": [[7, "datasets"]], "Dealing with class imbalance [video]": [[20, "dealing-with-class-imbalance-video"], [83, "dealing-with-class-imbalance-video"]], "Dealing with unknown categories": [[17, "dealing-with-unknown-categories"], [80, "dealing-with-unknown-categories"]], "Debugging": [[10, "debugging"]], "Decision boundaries playground": [[34, "decision-boundaries-playground"]], "Decision boundary": [[13, "decision-boundary"], [76, "decision-boundary"]], "Decision boundary for max_depth=1": [[13, "decision-boundary-for-max-depth-1"], [76, "decision-boundary-for-max-depth-1"]], "Decision boundary for max_depth=2": [[13, "decision-boundary-for-max-depth-2"], [76, "decision-boundary-for-max-depth-2"]], "Decision boundary for max_depth=5": [[13, "decision-boundary-for-max-depth-5"], [76, "decision-boundary-for-max-depth-5"]], "Decision boundary of SVMs": [[15, "decision-boundary-of-svms"], [78, "decision-boundary-of-svms"]], "Decision boundary of logistic regression": [[18, "decision-boundary-of-logistic-regression"], [81, "decision-boundary-of-logistic-regression"]], "Decision tree algorithm": [[13, "decision-tree-algorithm"], [76, "decision-tree-algorithm"]], "Decision tree feature importances": [[23, "decision-tree-feature-importances"], [43, "decision-tree-feature-importances"], [86, "decision-tree-feature-importances"]], "Decision tree for regression problems": [[13, "decision-tree-for-regression-problems"], [76, "decision-tree-for-regression-problems"]], "Decision tree model": [[33, "decision-tree-model"], [64, "decision-tree-model"]], "Decision tree with max_depth=1": [[13, "decision-tree-with-max-depth-1"], [76, "decision-tree-with-max-depth-1"]], "Decision tree with max_depth=3": [[13, "decision-tree-with-max-depth-3"], [76, "decision-tree-with-max-depth-3"]], "Decision trees [video]": [[13, "decision-trees-video"], [76, "decision-trees-video"]], "Decision trees with continuous features": [[13, "decision-trees-with-continuous-features"], [76, "decision-trees-with-continuous-features"]], "DecisionTreeClassifier baseline": [[22, "decisiontreeclassifier-baseline"], [42, "decisiontreeclassifier-baseline"], [85, "decisiontreeclassifier-baseline"]], "DecisionTreeClassifier on quiz2 grade prediction toy dataset": [[13, "decisiontreeclassifier-on-quiz2-grade-prediction-toy-dataset"], [76, "decisiontreeclassifier-on-quiz2-grade-prediction-toy-dataset"]], "Decisions involve a few key pieces": [[30, "decisions-involve-a-few-key-pieces"], [51, "decisions-involve-a-few-key-pieces"], [95, "decisions-involve-a-few-key-pieces"]], "Decreasing the threshold": [[20, "decreasing-the-threshold"], [40, "decreasing-the-threshold"], [83, "decreasing-the-threshold"]], "Deep learning": [[28, "deep-learning"], [49, "deep-learning"], [93, "deep-learning"]], "Deep learning software": [[27, "deep-learning-software"], [92, "deep-learning-software"]], "Deliverable due dates (tentative)": [[1, "deliverable-due-dates-tentative"]], "Demo of creating a new web service": [[96, "demo-of-creating-a-new-web-service"]], "Demo of feature engineering with numeric features": [[24, "demo-of-feature-engineering-with-numeric-features"], [44, "demo-of-feature-engineering-with-numeric-features"], [87, "demo-of-feature-engineering-with-numeric-features"]], "Demo: A more complicated dataset": [[28, "demo-a-more-complicated-dataset"], [49, "demo-a-more-complicated-dataset"], [93, "demo-a-more-complicated-dataset"]], "Demo: Deploying moment classification model": [[96, "demo-deploying-moment-classification-model"]], "Demo: Model interpretation of linear classifiers": [[36, "demo-model-interpretation-of-linear-classifiers"], [37, "demo-model-interpretation-of-linear-classifiers"]], "Dendrogram": [[26, "dendrogram"], [89, "dendrogram"]], "Deploying the API on a server": [[96, "deploying-the-api-on-a-server"]], "Deployment": [[99, "deployment"]], "Difference between Statistics and Machine Learning": [[96, "difference-between-statistics-and-machine-learning"]], "Different models": [[23, "different-models"], [43, "different-models"], [86, "different-models"]], "Different range for hyperparameters yields better results!": [[19, "different-range-for-hyperparameters-yields-better-results"], [38, "different-range-for-hyperparameters-yields-better-results"], [39, "different-range-for-hyperparameters-yields-better-results"], [82, "different-range-for-hyperparameters-yields-better-results"]], "Different scoring functions with cross_validate": [[21, "different-scoring-functions-with-cross-validate"], [41, "different-scoring-functions-with-cross-validate"], [84, "different-scoring-functions-with-cross-validate"]], "Dimensions in ML problems": [[15, "dimensions-in-ml-problems"], [78, "dimensions-in-ml-problems"]], "Discuss the following questions in your group": [[33, "discuss-the-following-questions-in-your-group"], [64, "discuss-the-following-questions-in-your-group"]], "Discussion": [[96, "discussion"]], "Discussion question": [[48, "discussion-question"], [91, "discussion-question"]], "Discussion questions": [[35, "discussion-questions"]], "Discussion questions:": [[30, "discussion-questions"], [51, "discussion-questions"], [95, "discussion-questions"]], "Distance between feature vectors": [[15, "distance-between-feature-vectors"], [78, "distance-between-feature-vectors"]], "Do we actually want to use certain features for prediction?": [[17, "do-we-actually-want-to-use-certain-features-for-prediction"], [80, "do-we-actually-want-to-use-certain-features-for-prediction"]], "Do we have class imbalance?": [[22, "do-we-have-class-imbalance"], [23, "do-we-have-class-imbalance"], [42, "do-we-have-class-imbalance"], [43, "do-we-have-class-imbalance"], [85, "do-we-have-class-imbalance"], [86, "do-we-have-class-imbalance"]], "Do we have correlated features?": [[23, "do-we-have-correlated-features"], [43, "do-we-have-correlated-features"], [86, "do-we-have-correlated-features"]], "Document clustering": [[25, "document-clustering"], [88, "document-clustering"]], "Domain-specific transformations": [[24, "domain-specific-transformations"], [87, "domain-specific-transformations"]], "Dummy Classifier": [[35, "dummy-classifier"]], "Dummy classifier": [[97, "dummy-classifier"]], "Dummy model": [[34, "dummy-model"]], "DummyClassifier": [[13, "dummyclassifier"], [28, "dummyclassifier"], [29, "dummyclassifier"], [49, "dummyclassifier"], [50, "dummyclassifier"], [76, "dummyclassifier"], [93, "dummyclassifier"], [94, "dummyclassifier"]], "DummyClassifier baseline": [[22, "dummyclassifier-baseline"], [42, "dummyclassifier-baseline"], [85, "dummyclassifier-baseline"]], "DummyClassifier on quiz2 grade prediction toy dataset": [[13, "dummyclassifier-on-quiz2-grade-prediction-toy-dataset"], [76, "dummyclassifier-on-quiz2-grade-prediction-toy-dataset"]], "DummyRegressor": [[13, "dummyregressor"], [21, "dummyregressor"], [41, "dummyregressor"], [76, "dummyregressor"], [84, "dummyregressor"]], "EDA": [[16, "eda"], [20, "eda"], [21, "eda"], [40, "eda"], [41, "eda"], [79, "eda"], [83, "eda"], [84, "eda"]], "Encoding text data": [[80, "encoding-text-data"]], "Encoding text data (Video)": [[17, "encoding-text-data-video"]], "Encoding time as a number": [[28, "encoding-time-as-a-number"], [49, "encoding-time-as-a-number"], [93, "encoding-time-as-a-number"]], "Encoding time of day as a categorical feature": [[28, "encoding-time-of-day-as-a-categorical-feature"], [49, "encoding-time-of-day-as-a-categorical-feature"], [93, "encoding-time-of-day-as-a-categorical-feature"]], "Ensembles": [[99, "ensembles"]], "Equally good": [[30, "equally-good"], [51, "equally-good"], [95, "equally-good"]], "Ethics": [[99, "ethics"]], "Euclidean distance": [[15, "euclidean-distance"], [78, "euclidean-distance"]], "Evaluating DBSCAN clusters": [[26, "evaluating-dbscan-clusters"], [89, "evaluating-dbscan-clusters"]], "Evaluation": [[47, "evaluation"], [47, "id3"], [90, "evaluation"], [90, "id3"]], "Evaluation metrics": [[99, "evaluation-metrics"]], "Evaluation metrics for binary classification: Motivation": [[20, "evaluation-metrics-for-binary-classification-motivation"], [40, "evaluation-metrics-for-binary-classification-motivation"], [83, "evaluation-metrics-for-binary-classification-motivation"]], "Evalution metrics overview": [[20, "evalution-metrics-overview"], [40, "evalution-metrics-overview"], [83, "evalution-metrics-overview"]], "Examining learned coefficients": [[36, "examining-learned-coefficients"], [37, "examining-learned-coefficients"]], "Examining the preprocessed data": [[21, "examining-the-preprocessed-data"], [30, "examining-the-preprocessed-data"], [41, "examining-the-preprocessed-data"], [51, "examining-the-preprocessed-data"], [84, "examining-the-preprocessed-data"], [95, "examining-the-preprocessed-data"]], "Examining the vocabulary": [[36, "examining-the-vocabulary"], [37, "examining-the-vocabulary"]], "Example": [[18, "example"], [22, "example"], [42, "example"], [81, "example"], [85, "example"]], "Example 1": [[23, "example-1"]], "Example 1: Predicting whether a patient has a liver disease or not": [[12, "example-1-predicting-whether-a-patient-has-a-liver-disease-or-not"], [75, "example-1-predicting-whether-a-patient-has-a-liver-disease-or-not"]], "Example 1: What is \u201ccorrect\u201d grouping?": [[25, "example-1-what-is-correct-grouping"], [88, "example-1-what-is-correct-grouping"]], "Example 1: quiz 2 grade prediction": [[13, "example-1-quiz-2-grade-prediction"], [76, "example-1-quiz-2-grade-prediction"]], "Example 2": [[23, "example-2"]], "Example 2: Predicting country using the longitude and latitude": [[13, "example-2-predicting-country-using-the-longitude-and-latitude"], [76, "example-2-predicting-country-using-the-longitude-and-latitude"]], "Example 2: Predicting the label of a given image": [[12, "example-2-predicting-the-label-of-a-given-image"], [75, "example-2-predicting-the-label-of-a-given-image"]], "Example 3: Predicting sentiment expressed in a movie review": [[12, "example-3-predicting-sentiment-expressed-in-a-movie-review"], [75, "example-3-predicting-sentiment-expressed-in-a-movie-review"]], "Example 4: Predicting housing prices": [[12, "example-4-predicting-housing-prices"], [75, "example-4-predicting-housing-prices"]], "Example showing how can we interpret coefficients of scaled features.": [[23, "example-showing-how-can-we-interpret-coefficients-of-scaled-features"], [43, "example-showing-how-can-we-interpret-coefficients-of-scaled-features"], [86, "example-showing-how-can-we-interpret-coefficients-of-scaled-features"]], "Example: Is \u201cRelevance\u201d clearly defined?": [[24, "example-is-relevance-clearly-defined"], [87, "example-is-relevance-clearly-defined"]], "Example: Predict whether a message is spam or not": [[12, "example-predict-whether-a-message-is-spam-or-not"], [75, "example-predict-whether-a-message-is-spam-or-not"]], "Example: Supervised vs unsupervised learning": [[25, "example-supervised-vs-unsupervised-learning"], [88, "example-supervised-vs-unsupervised-learning"]], "Example: Tabular data for grade prediction": [[13, "example-tabular-data-for-grade-prediction"], [76, "example-tabular-data-for-grade-prediction"]], "Example: Tabular data for the housing price prediction": [[13, "example-tabular-data-for-the-housing-price-prediction"], [76, "example-tabular-data-for-the-housing-price-prediction"]], "Example: class_weight parameter of sklearn LogisticRegression": [[20, "example-class-weight-parameter-of-sklearn-logisticregression"], [83, "example-class-weight-parameter-of-sklearn-logisticregression"]], "Example: k-nearest neighbours on the Spotify dataset": [[16, "example-k-nearest-neighbours-on-the-spotify-dataset"], [79, "example-k-nearest-neighbours-on-the-spotify-dataset"]], "Examples": [[12, "examples"], [75, "examples"]], "Exercise 17.1 Select all of the following statements which are True (iClicker)": [[47, "exercise-17-1-select-all-of-the-following-statements-which-are-true-iclicker"], [47, "id2"], [90, "exercise-17-1-select-all-of-the-following-statements-which-are-true-iclicker"]], "Exercise 17.2 Select all of the following statements which are True (iClicker)": [[90, "exercise-17-2-select-all-of-the-following-statements-which-are-true-iclicker"]], "Exercise 2.1 Select all of the following statements which are examples of supervised machine learning": [[13, "exercise-2-1-select-all-of-the-following-statements-which-are-examples-of-supervised-machine-learning"], [76, "exercise-2-1-select-all-of-the-following-statements-which-are-examples-of-supervised-machine-learning"]], "Exercise 2.4": [[13, "exercise-2-4"], [76, "exercise-2-4"]], "Exercise 8.2": [[19, "exercise-8-2"], [82, "exercise-8-2"]], "Exhaustive grid search: sklearn.model_selection.GridSearchCV": [[19, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"], [38, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"], [39, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"], [82, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"]], "Explaining a prediction": [[23, "explaining-a-prediction"], [43, "explaining-a-prediction"], [86, "explaining-a-prediction"]], "Explanation 1": [[30, "explanation-1"], [51, "explanation-1"], [95, "explanation-1"]], "Explanation 2": [[30, "explanation-2"], [51, "explanation-2"], [95, "explanation-2"]], "Exploratory Data Analysis": [[33, "exploratory-data-analysis"], [64, "exploratory-data-analysis"]], "Exploratory data analysis": [[28, "exploratory-data-analysis"], [35, "exploratory-data-analysis"], [49, "exploratory-data-analysis"], [93, "exploratory-data-analysis"]], "Extracting BOW features using scikit-learn": [[17, "extracting-bow-features-using-scikit-learn"], [80, "extracting-bow-features-using-scikit-learn"]], "Extracting date and time information": [[28, "extracting-date-and-time-information"], [49, "extracting-date-and-time-information"], [93, "extracting-date-and-time-information"]], "F1-score": [[20, "f1-score"], [40, "f1-score"], [83, "f1-score"]], "Faster method: vectorize the loop over rows": [[8, "faster-method-vectorize-the-loop-over-rows"]], "Fastest method: broadcasting": [[8, "fastest-method-broadcasting"]], "Feature crosses for one-hot encoded features": [[24, "feature-crosses-for-one-hot-encoded-features"], [44, "feature-crosses-for-one-hot-encoded-features"], [87, "feature-crosses-for-one-hot-encoded-features"]], "Feature engineering": [[28, "feature-engineering"], [49, "feature-engineering"], [93, "feature-engineering"]], "Feature engineering and selection": [[99, "feature-engineering-and-selection"]], "Feature engineering for date/time columns": [[28, "feature-engineering-for-date-time-columns"], [49, "feature-engineering-for-date-time-columns"], [93, "feature-engineering-for-date-time-columns"]], "Feature engineering: Encoding date/time as feature(s)": [[28, "feature-engineering-encoding-date-time-as-feature-s"], [49, "feature-engineering-encoding-date-time-as-feature-s"], [93, "feature-engineering-encoding-date-time-as-feature-s"]], "Feature engineering: Motivation": [[24, "feature-engineering-motivation"], [87, "feature-engineering-motivation"]], "Feature importances": [[23, "feature-importances"], [43, "feature-importances"], [86, "feature-importances"], [99, "feature-importances"]], "Feature importances in linear models": [[23, "feature-importances-in-linear-models"], [23, "id2"], [43, "feature-importances-in-linear-models"], [43, "id2"], [86, "feature-importances-in-linear-models"], [86, "id2"]], "Feature interactions and feature crosses": [[24, "feature-interactions-and-feature-crosses"], [44, "feature-interactions-and-feature-crosses"], [87, "feature-interactions-and-feature-crosses"]], "Feature names of transformed data": [[21, "feature-names-of-transformed-data"], [41, "feature-names-of-transformed-data"], [84, "feature-names-of-transformed-data"]], "Feature selection: Introduction and motivation": [[24, "feature-selection-introduction-and-motivation"], [44, "feature-selection-introduction-and-motivation"], [87, "feature-selection-introduction-and-motivation"]], "Feature transformations and the golden rule": [[16, "feature-transformations-and-the-golden-rule"], [79, "feature-transformations-and-the-golden-rule"]], "Feature types": [[21, "feature-types"], [21, "id1"], [30, "feature-types"], [41, "feature-types"], [41, "id1"], [51, "feature-types"], [84, "feature-types"], [84, "id1"], [95, "feature-types"]], "Feature vectors": [[15, "feature-vectors"], [78, "feature-vectors"]], "Figures": [[7, "figures"]], "Filtering a dataframe with [] and df.query()": [[8, "filtering-a-dataframe-with-and-df-query"]], "Final comments and summary": [[19, "final-comments-and-summary"], [47, "final-comments-and-summary"], [82, "final-comments-and-summary"], [90, "final-comments-and-summary"]], "Final comments, summary, and reflection": [[13, "final-comments-summary-and-reflection"], [25, "final-comments-summary-and-reflection"], [26, "final-comments-summary-and-reflection"], [76, "final-comments-summary-and-reflection"], [88, "final-comments-summary-and-reflection"], [89, "final-comments-summary-and-reflection"]], "Final exam": [[101, "final-exam"]], "Final exam review guiding questions": [[99, null]], "Final remarks": [[28, "final-remarks"], [49, "final-remarks"], [93, "final-remarks"]], "Finding the distances to a query point": [[15, "finding-the-distances-to-a-query-point"], [78, "finding-the-distances-to-a-query-point"]], "Finding the nearest neighbour": [[15, "finding-the-nearest-neighbour"], [78, "finding-the-nearest-neighbour"]], "First deliverables": [[12, "first-deliverables"]], "Forecasting further into the future": [[28, "forecasting-further-into-the-future"], [49, "forecasting-further-into-the-future"], [93, "forecasting-further-into-the-future"]], "Forecasting further into the future on a retail dataset": [[28, "forecasting-further-into-the-future-on-a-retail-dataset"], [49, "forecasting-further-into-the-future-on-a-retail-dataset"], [93, "forecasting-further-into-the-future-on-a-retail-dataset"]], "Formulating the problem of recommender systems": [[47, "formulating-the-problem-of-recommender-systems"], [90, "formulating-the-problem-of-recommender-systems"]], "GB better than RF": [[30, "gb-better-than-rf"], [51, "gb-better-than-rf"], [95, "gb-better-than-rf"]], "Garbage in, garbage out.": [[24, "garbage-in-garbage-out"], [87, "garbage-in-garbage-out"]], "General advice on finding relevant features": [[24, "general-advice-on-finding-relevant-features"], [87, "general-advice-on-finding-relevant-features"]], "General guidelines": [[6, "general-guidelines"]], "General idea": [[22, "general-idea"], [42, "general-idea"], [85, "general-idea"]], "General idea of k-nearest neighbours algorithm": [[15, "general-idea-of-k-nearest-neighbours-algorithm"], [78, "general-idea-of-k-nearest-neighbours-algorithm"]], "General idea of search and score methods": [[24, "general-idea-of-search-and-score-methods"], [87, "general-idea-of-search-and-score-methods"]], "General questions": [[4, "general-questions"]], "Generalization [video]": [[14, "generalization-video"], [77, "generalization-video"]], "Generalization: Fundamental goal of ML": [[14, "generalization-fundamental-goal-of-ml"], [77, "generalization-fundamental-goal-of-ml"]], "Generalizing to more features": [[18, "generalizing-to-more-features"], [81, "generalizing-to-more-features"]], "Generalizing to unseen data": [[14, "generalizing-to-unseen-data"], [77, "generalizing-to-unseen-data"]], "Geometric view of tabular data and dimensions": [[15, "geometric-view-of-tabular-data-and-dimensions"], [78, "geometric-view-of-tabular-data-and-dimensions"]], "Git": [[10, "git"]], "GitHub Desktop": [[5, "github-desktop"]], "Global average baseline": [[47, "global-average-baseline"], [90, "global-average-baseline"]], "Golden rule violation: Example 1": [[14, "golden-rule-violation-example-1"], [77, "golden-rule-violation-example-1"]], "Golden rule violation: Example 2": [[14, "golden-rule-violation-example-2"], [77, "golden-rule-violation-example-2"]], "Grades": [[12, "grades"]], "Gradient boosted trees [video]": [[22, "gradient-boosted-trees-video"], [42, "gradient-boosted-trees-video"], [85, "gradient-boosted-trees-video"]], "Gradient boosting in sklearn": [[22, "gradient-boosting-in-sklearn"], [42, "gradient-boosting-in-sklearn"], [85, "gradient-boosting-in-sklearn"]], "Grading concerns: time limit": [[6, "grading-concerns-time-limit"]], "Grading scheme": [[101, "grading-scheme"]], "Grading-related questions": [[4, "grading-related-questions"]], "Handling imbalance": [[20, "handling-imbalance"], [83, "handling-imbalance"]], "Here is the workflow we\u2019ll generally follow.": [[14, "here-is-the-workflow-we-ll-generally-follow"], [77, "here-is-the-workflow-we-ll-generally-follow"]], "Hierarchical clustering": [[46, "hierarchical-clustering"]], "Hierarchical clustering [video]": [[26, "hierarchical-clustering-video"], [89, "hierarchical-clustering-video"]], "Homework info & submission guidelines": [[7, null]], "How are we making predictions?": [[18, "how-are-we-making-predictions"], [81, "how-are-we-making-predictions"]], "How can we avoid violating golden rule?": [[14, "how-can-we-avoid-violating-golden-rule"], [77, "how-can-we-avoid-violating-golden-rule"]], "How can we get feature importances for non sklearn models?": [[23, "how-can-we-get-feature-importances-for-non-sklearn-models"], [43, "how-can-we-get-feature-importances-for-non-sklearn-models"], [86, "how-can-we-get-feature-importances-for-non-sklearn-models"]], "How do they work?": [[22, "how-do-they-work"], [42, "how-do-they-work"], [85, "how-do-they-work"]], "How do we carry out feature selection?": [[24, "how-do-we-carry-out-feature-selection"], [44, "how-do-we-carry-out-feature-selection"], [87, "how-do-we-carry-out-feature-selection"]], "How does fit work?": [[13, "how-does-fit-work"], [13, "id2"], [76, "how-does-fit-work"], [76, "id2"]], "How does it work?": [[26, "how-does-it-work"], [30, "how-does-it-work"], [51, "how-does-it-work"], [89, "how-does-it-work"], [95, "how-does-it-work"]], "How does logistic regression calculate these probabilities?": [[18, "how-does-logistic-regression-calculate-these-probabilities"], [81, "how-does-logistic-regression-calculate-these-probabilities"]], "How does predict work?": [[13, "how-does-predict-work"], [76, "how-does-predict-work"]], "How to approximate generalization error?": [[14, "how-to-approximate-generalization-error"], [77, "how-to-approximate-generalization-error"]], "How to ask for help": [[4, null]], "How to carry out cross-validation?": [[16, "how-to-carry-out-cross-validation"], [79, "how-to-carry-out-cross-validation"]], "How to choose n_neighbors?": [[15, "how-to-choose-n-neighbors"], [78, "how-to-choose-n-neighbors"]], "How to pick a model that would generalize better?": [[14, "how-to-pick-a-model-that-would-generalize-better"], [77, "how-to-pick-a-model-that-would-generalize-better"]], "How to submit": [[7, "how-to-submit"]], "How would you do it properly? 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"Imputation": [[16, "imputation"], [79, "imputation"]], "Imputation and scaling [video]": [[16, "imputation-and-scaling-video"], [79, "imputation-and-scaling-video"]], "Incorporating ordinal feature class_attendance": [[17, "incorporating-ordinal-feature-class-attendance"], [80, "incorporating-ordinal-feature-class-attendance"]], "Incorporating text features": [[35, "incorporating-text-features"]], "Increasing the threshold": [[20, "increasing-the-threshold"], [40, "increasing-the-threshold"], [83, "increasing-the-threshold"]], "Indexing Dataframes": [[8, "indexing-dataframes"]], "Indexing cheatsheet": [[8, "indexing-cheatsheet"]], "Inertia": [[25, "inertia"], [88, "inertia"]], "Initial analysis, EDA, preprocessing": [[96, "initial-analysis-eda-preprocessing"]], "Initialization of K-Means": [[25, "initialization-of-k-means"], [88, "initialization-of-k-means"]], "Inject randomness in the classifier construction": [[22, "inject-randomness-in-the-classifier-construction"], [42, 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loop": [[8, "slowest-method-nested-loop"]], "Software": [[0, "software"]], "Some important hyperparameters:": [[22, "some-important-hyperparameters"], [42, "some-important-hyperparameters"], [85, "some-important-hyperparameters"]], "Some key takeaways": [[96, "some-key-takeaways"]], "Some quotes on feature engineering": [[24, "some-quotes-on-feature-engineering"], [87, "some-quotes-on-feature-engineering"]], "Some terminology related to trees": [[13, "some-terminology-related-to-trees"], [76, "some-terminology-related-to-trees"]], "Some ways to pick hyperparameters:": [[19, "some-ways-to-pick-hyperparameters"], [82, "some-ways-to-pick-hyperparameters"]], "Sorting a dataframe with df.sort_values()": [[8, "sorting-a-dataframe-with-df-sort-values"]], "Spam/non spam toy example": [[17, "spam-non-spam-toy-example"], [80, "spam-non-spam-toy-example"]], "Specific questions": [[4, "specific-questions"]], "Stacking": [[22, "stacking"], [42, "stacking"], [85, "stacking"]], "Steps to train a classifier using sklearn": [[13, "steps-to-train-a-classifier-using-sklearn"], [76, "steps-to-train-a-classifier-using-sklearn"]], "Stratified Splits": [[20, "stratified-splits"], [83, "stratified-splits"]], "Strengths and weaknesses": [[22, "strengths-and-weaknesses"], [42, "strengths-and-weaknesses"], [85, "strengths-and-weaknesses"]], "Strengths of linear models": [[18, "strengths-of-linear-models"], [81, "strengths-of-linear-models"]], "Study tips": [[99, "study-tips"]], "Submitting on Gradescope": [[7, "submitting-on-gradescope"]], "Summary": [[12, "summary"], [15, "summary"], [22, "summary"], [27, "summary"], [29, "summary"], [42, "summary"], [48, "summary"], [50, "summary"], [75, "summary"], [78, "summary"], [85, "summary"], [91, "summary"], [92, "summary"], [94, "summary"]], "Summary and reflection": [[14, "summary-and-reflection"], [77, "summary-and-reflection"]], "Summary of linear models": [[18, "summary-of-linear-models"], [81, "summary-of-linear-models"]], "Summary of train, validation, test, and deployment data": [[14, "summary-of-train-validation-test-and-deployment-data"], [77, "summary-of-train-validation-test-and-deployment-data"]], "Summary: Pros and cons": [[26, "summary-pros-and-cons"], [89, "summary-pros-and-cons"]], "Supervised approach to rating prediction": [[47, "supervised-approach-to-rating-prediction"], [90, "supervised-approach-to-rating-prediction"]], "Supervised learning": [[25, "supervised-learning"], [88, "supervised-learning"]], "Supervised learning (Reminder)": [[13, "supervised-learning-reminder"], [76, "supervised-learning-reminder"]], "Supervised learning vs. Unsupervised learning": [[13, "supervised-learning-vs-unsupervised-learning"], [76, "supervised-learning-vs-unsupervised-learning"]], "Supervised machine learning": [[12, "supervised-machine-learning"], [75, "supervised-machine-learning"]], "Support Vector Machines (SVMs) with RBF kernel [video]": [[15, "support-vector-machines-svms-with-rbf-kernel-video"], [78, "support-vector-machines-svms-with-rbf-kernel-video"]], "Support vectors": [[15, "support-vectors"], [78, "support-vectors"]], "Survival analysis": [[99, "survival-analysis"]], "Survival plots": [[29, "survival-plots"], [50, "survival-plots"], [94, "survival-plots"]], "Syllabus": [[1, "syllabus"], [101, null]], "TAs": [[1, "tas"], [101, "tas"]], "Tabular data": [[13, "tabular-data"], [76, "tabular-data"]], "Take-home message": [[26, "take-home-message"], [89, "take-home-message"]], "Teaching Team": [[101, "teaching-team"]], "Terminology": [[27, "terminology"], [92, "terminology"]], "Terminology [video]": [[13, "terminology-video"], [76, "terminology-video"]], "Testing your git installation": [[5, "testing-your-git-installation"]], "The Dataset": [[64, "the-dataset"]], "The Netflix prize": [[22, "the-netflix-prize"], [85, "the-netflix-prize"]], "The __ syntax": [[19, "the-syntax"], [38, "the-syntax"], [39, "the-syntax"], [82, "the-syntax"]], "The best features may be dependent on the model you use.": [[24, "the-best-features-may-be-dependent-on-the-model-you-use"], [87, "the-best-features-may-be-dependent-on-the-model-you-use"]], "The golden rule ": [[14, "the-golden-rule"], [77, "the-golden-rule"]], "The random forests classifier": [[22, "the-random-forests-classifier"], [42, "the-random-forests-classifier"], [85, "the-random-forests-classifier"]], "The sigmoid function": [[18, "the-sigmoid-function"], [81, "the-sigmoid-function"]], "The teaching team": [[1, "the-teaching-team"]], "The \u201cfundamental tradeoff\u201d of supervised learning:": [[14, "the-fundamental-tradeoff-of-supervised-learning"], [77, "the-fundamental-tradeoff-of-supervised-learning"]], "The \u201cperfect\u201d spaghetti sauce": [[25, "the-perfect-spaghetti-sauce"], [88, "the-perfect-spaghetti-sauce"]], "Things to watch out for": [[30, "things-to-watch-out-for"], [51, "things-to-watch-out-for"], [95, "things-to-watch-out-for"]], "Thresholding": [[40, "thresholding"]], "Time series": [[99, "time-series"]], "Time to event and censoring": [[29, "time-to-event-and-censoring"], [50, "time-to-event-and-censoring"], [94, "time-to-event-and-censoring"]], "Tokenization": [[48, "tokenization"], [91, "tokenization"]], "Topic modeling": [[48, "topic-modeling"], [91, "topic-modeling"]], "Topic modeling motivation": [[48, "topic-modeling-motivation"], [91, "topic-modeling-motivation"]], "Topic modeling pipeline": [[48, "topic-modeling-pipeline"], [91, "topic-modeling-pipeline"]], "Topic modeling toy example": [[48, "topic-modeling-toy-example"], [91, "topic-modeling-toy-example"]], "Toy datasets": [[13, "toy-datasets"], [76, "toy-datasets"]], "Traditional time series approaches": [[28, "traditional-time-series-approaches"], [49, "traditional-time-series-approaches"], [93, "traditional-time-series-approaches"]], "Train/test split for temporal data": [[28, "train-test-split-for-temporal-data"], [49, "train-test-split-for-temporal-data"], [93, "train-test-split-for-temporal-data"]], "Train/test splits": [[28, "train-test-splits"], [49, "train-test-splits"], [93, "train-test-splits"]], "Train/validation/test split": [[14, "train-validation-test-split"], [77, "train-validation-test-split"]], "Training a supervised machine learning model with X and y": [[12, "training-a-supervised-machine-learning-model-with-x-and-y"], [75, "training-a-supervised-machine-learning-model-with-x-and-y"]], "Training data for the motivating example": [[18, "training-data-for-the-motivating-example"], [81, "training-data-for-the-motivating-example"]], "Training error vs. Generalization error": [[14, "training-error-vs-generalization-error"], [77, "training-error-vs-generalization-error"]], "Training models with transformed data": [[17, "training-models-with-transformed-data"], [80, "training-models-with-transformed-data"]], "Training on the full corpus": [[96, "training-on-the-full-corpus"]], "Training random forests and gradient boosted trees": [[30, "training-random-forests-and-gradient-boosted-trees"], [51, "training-random-forests-and-gradient-boosted-trees"], [95, "training-random-forests-and-gradient-boosted-trees"]], "Transfer learning": [[27, "transfer-learning"], [92, "transfer-learning"]], "Transformations on the toy data": [[17, "transformations-on-the-toy-data"], [80, "transformations-on-the-toy-data"]], "Transforming the targets": [[21, "transforming-the-targets"], [41, "transforming-the-targets"], [84, "transforming-the-targets"]], "Transparency and explainability of ML models: Motivation": [[23, "transparency-and-explainability-of-ml-models-motivation"], [43, "transparency-and-explainability-of-ml-models-motivation"], [86, "transparency-and-explainability-of-ml-models-motivation"]], "Tree-based ensemble models": [[22, "tree-based-ensemble-models"], [85, "tree-based-ensemble-models"]], "Tree-based models": [[22, "tree-based-models"], [85, "tree-based-models"]], "Try out this moment predictor": [[96, "try-out-this-moment-predictor"]], "Tuning alpha hyperparameter of Ridge": [[21, "tuning-alpha-hyperparameter-of-ridge"], [41, "tuning-alpha-hyperparameter-of-ridge"], [84, "tuning-alpha-hyperparameter-of-ridge"]], "Types of censoring": [[29, "types-of-censoring"], [50, "types-of-censoring"], [94, "types-of-censoring"]], "Types of errors": [[14, "types-of-errors"], [77, "types-of-errors"]], "Types of machine learning": [[12, "types-of-machine-learning"], [25, "types-of-machine-learning"], [75, "types-of-machine-learning"], [88, "types-of-machine-learning"]], "Types of problems involving time series": [[28, "types-of-problems-involving-time-series"], [49, "types-of-problems-involving-time-series"], [93, "types-of-problems-involving-time-series"]], "Types of questions we might want to answer:": [[29, "types-of-questions-we-might-want-to-answer"], [50, "types-of-questions-we-might-want-to-answer"], [94, "types-of-questions-we-might-want-to-answer"]], "Typical steps to build a supervised machine learning model": [[33, "typical-steps-to-build-a-supervised-machine-learning-model"], [64, "typical-steps-to-build-a-supervised-machine-learning-model"]], "UBC CPSC 330: Applied Machine Learning (2024W1)": [[1, null]], "Ubuntu Users": [[5, "ubuntu-users"]], "Underfitting": [[14, "underfitting"], [77, "underfitting"]], "Underfitting, overfitting, the fundamental trade-off, the golden rule [video]": [[14, "underfitting-overfitting-the-fundamental-trade-off-the-golden-rule-video"], [77, "underfitting-overfitting-the-fundamental-trade-off-the-golden-rule-video"]], "Undersampling": [[20, "undersampling"], [83, "undersampling"]], "Understanding the problem": [[96, "understanding-the-problem"]], "Unequally spaced time points": [[28, "unequally-spaced-time-points"], [49, "unequally-spaced-time-points"], [93, "unequally-spaced-time-points"]], "Unsupervised learning": [[25, "unsupervised-learning"], [88, "unsupervised-learning"]], "Updates to assignments": [[7, "updates-to-assignments"]], "Use of AI in the course": [[101, "use-of-ai-in-the-course"]], "Use our template to create a repository": [[7, "use-our-template-to-create-a-repository"]], "Using OVR and OVO as wrappers": [[98, "using-ovr-and-ovo-as-wrappers"]], "Using SMOTE": [[20, "using-smote"], [83, "using-smote"]], "Using Silhouette scores to select the number of clusters": [[25, "using-silhouette-scores-to-select-the-number-of-clusters"], [88, "using-silhouette-scores-to-select-the-number-of-clusters"]], "Using multiple metrics in GridSearchCV or RandomizedSearchCV": [[21, "using-multiple-metrics-in-gridsearchcv-or-randomizedsearchcv"], [41, "using-multiple-metrics-in-gridsearchcv-or-randomizedsearchcv"], [84, "using-multiple-metrics-in-gridsearchcv-or-randomizedsearchcv"]], "Using pre-trained models as feature extractor": [[27, "using-pre-trained-models-as-feature-extractor"], [92, "using-pre-trained-models-as-feature-extractor"]], "Using pre-trained models out-of-the-box": [[27, "using-pre-trained-models-out-of-the-box"], [92, "using-pre-trained-models-out-of-the-box"]], "Using regression metrics with scikit-learn": [[21, "using-regression-metrics-with-scikit-learn"], [41, "using-regression-metrics-with-scikit-learn"], [84, "using-regression-metrics-with-scikit-learn"]], "Viewing the transformed data as a dataframe": [[17, "viewing-the-transformed-data-as-a-dataframe"], [80, "viewing-the-transformed-data-as-a-dataframe"]], "Virtual environment": [[10, "virtual-environment"]], "Visualization": [[9, "visualization"]], "Visualizing the parameter grid as a heatmap": [[19, "visualizing-the-parameter-grid-as-a-heatmap"], [38, "visualizing-the-parameter-grid-as-a-heatmap"], [39, "visualizing-the-parameter-grid-as-a-heatmap"], [82, "visualizing-the-parameter-grid-as-a-heatmap"]], "Visualizing your results": [[30, "visualizing-your-results"], [51, "visualizing-your-results"], [95, "visualizing-your-results"]], "Warning": [[13, null], [76, null]], "Warnings about feature selection": [[24, "warnings-about-feature-selection"], [24, "id8"], [44, "warnings-about-feature-selection"], [87, "warnings-about-feature-selection"], [87, "id8"]], "Weaknesses": [[22, "weaknesses"], [42, "weaknesses"], [85, "weaknesses"]], "Web app on a real server": [[96, "web-app-on-a-real-server"]], "Web app on local server": [[96, "web-app-on-local-server"]], "What all transformations we need to apply on the dataset?": [[79, "what-all-transformations-we-need-to-apply-on-the-dataset"]], "What and Why": [[10, "what-and-why"]], "What are git and GitHub?": [[5, null]], "What are the options?": [[16, "what-are-the-options"], [79, "what-are-the-options"]], "What are we exactly learning?": [[18, "what-are-we-exactly-learning"], [81, "what-are-we-exactly-learning"]], "What did we cover?": [[47, "what-did-we-cover"], [90, "what-did-we-cover"], [96, "what-did-we-cover"]], "What did we learn today?": [[14, "what-did-we-learn-today"], [16, "what-did-we-learn-today"], [17, "what-did-we-learn-today"], [20, "what-did-we-learn-today"], [21, "what-did-we-learn-today"], [30, "what-did-we-learn-today"], [51, "what-did-we-learn-today"], [77, "what-did-we-learn-today"], [79, "what-did-we-learn-today"], [80, "what-did-we-learn-today"], [83, "what-did-we-learn-today"], [84, "what-did-we-learn-today"], [95, "what-did-we-learn-today"]], "What does this have to do with applied ML?": [[30, "what-does-this-have-to-do-with-applied-ml"], [51, "what-does-this-have-to-do-with-applied-ml"], [95, "what-does-this-have-to-do-with-applied-ml"]], "What does this mean for us, when we\u2019re trying to make claims about our data?": [[30, "what-does-this-mean-for-us-when-we-re-trying-to-make-claims-about-our-data"], [51, "what-does-this-mean-for-us-when-we-re-trying-to-make-claims-about-our-data"], [95, "what-does-this-mean-for-us-when-we-re-trying-to-make-claims-about-our-data"]], "What if we apply OHE?": [[17, "what-if-we-apply-ohe"], [80, "what-if-we-apply-ohe"]], "What is Natural Language Processing (NLP)?": [[48, "what-is-natural-language-processing-nlp"], [91, "what-is-natural-language-processing-nlp"]], "What is a recommender system?": [[47, "what-is-a-recommender-system"], [90, "what-is-a-recommender-system"]], "What is clustering?": [[25, "what-is-clustering"], [88, "what-is-clustering"]], "What is deployment?": [[96, "what-is-deployment"]], "What is feature engineering?": [[24, "what-is-feature-engineering"], [87, "what-is-feature-engineering"]], "What is feature selection?": [[24, "what-is-feature-selection"], [44, "what-is-feature-selection"], [87, "what-is-feature-selection"]], "What is grid search?": [[30, "what-is-grid-search"], [51, "what-is-grid-search"], [95, "what-is-grid-search"]], "What is model interpretability?": [[23, "what-is-model-interpretability"], [43, "what-is-model-interpretability"], [86, "what-is-model-interpretability"]], "What is supervised machine learning (ML)?": [[12, "what-is-supervised-machine-learning-ml"], [75, "what-is-supervised-machine-learning-ml"]], "What is \u201cpositive\u201d and \u201cnegative\u201d?": [[20, "what-is-positive-and-negative"], [40, "what-is-positive-and-negative"], [83, "what-is-positive-and-negative"]], "What kind of estimators can we combine?": [[22, "what-kind-of-estimators-can-we-combine"], [42, "what-kind-of-estimators-can-we-combine"], [85, "what-kind-of-estimators-can-we-combine"]], "What next?": [[96, "what-next"]], "What should be the loss? (Activity: 4 mins)": [[30, "what-should-be-the-loss-activity-4-mins"], [51, "what-should-be-the-loss-activity-4-mins"], [95, "what-should-be-the-loss-activity-4-mins"]], "What to look for in these plots?": [[25, "what-to-look-for-in-these-plots"], [88, "what-to-look-for-in-these-plots"]], "What transformations we need to apply on the dataset?": [[16, "what-transformations-we-need-to-apply-on-the-dataset"]], "What would I do differently?": [[96, "what-would-i-do-differently"]], "What\u2019s the Question?": [[64, "what-s-the-question"]], "What\u2019s the problem?": [[16, "what-s-the-problem"], [79, "what-s-the-problem"]], "When can we use broadcasting?": [[8, "when-can-we-use-broadcasting"]], "When experimenting, show the results asap": [[30, "when-experimenting-show-the-results-asap"], [51, "when-experimenting-show-the-results-asap"], [95, "when-experimenting-show-the-results-asap"]], "When is it OK to do things before splitting?": [[16, "when-is-it-ok-to-do-things-before-splitting"], [79, "when-is-it-ok-to-do-things-before-splitting"]], "When test score is much lower than CV score": [[19, "when-test-score-is-much-lower-than-cv-score"], [82, "when-test-score-is-much-lower-than-cv-score"]], "Which model is doing better in this scenario: SVC or Logistic Regression?": [[40, "which-model-is-doing-better-in-this-scenario-svc-or-logistic-regression"]], "Which model should I use?": [[22, "which-model-should-i-use"], [42, "which-model-should-i-use"], [85, "which-model-should-i-use"]], "Which type of error is more important?": [[20, "which-type-of-error-is-more-important"], [83, "which-type-of-error-is-more-important"]], "Which types of errors would be most critical for the bank to address?": [[40, "which-types-of-errors-would-be-most-critical-for-the-bank-to-address"]], "Why do we need a test set?": [[19, "why-do-we-need-a-test-set"], [82, "why-do-we-need-a-test-set"]], "Why do we want this information?": [[23, "why-do-we-want-this-information"], [43, "why-do-we-want-this-information"], [86, "why-do-we-want-this-information"]], "Why feature selection?": [[24, "why-feature-selection"], [44, "why-feature-selection"], [87, "why-feature-selection"]], "Why machine learning (ML)? [video]": [[12, "why-machine-learning-ml-video"], [75, "why-machine-learning-ml-video"]], "Why model transparency/interpretability?": [[23, "why-model-transparency-interpretability"], [43, "why-model-transparency-interpretability"], [86, "why-model-transparency-interpretability"]], "Why neural networks?": [[27, "why-neural-networks"], [27, "id1"], [92, "why-neural-networks"], [92, "id1"]], "Why not neural networks?": [[27, "why-not-neural-networks"], [27, "id2"], [92, "why-not-neural-networks"], [92, "id2"]], "Why should I use it?": [[30, "why-should-i-use-it"], [51, "why-should-i-use-it"], [95, "why-should-i-use-it"]], "Why should we care about effective communication?": [[30, "why-should-we-care-about-effective-communication"], [51, "why-should-we-care-about-effective-communication"], [95, "why-should-we-care-about-effective-communication"]], "Why should we care about recommendation systems?": [[47, "why-should-we-care-about-recommendation-systems"], [90, "why-should-we-care-about-recommendation-systems"]], "Why sparse matrices?": [[17, "why-sparse-matrices"], [80, "why-sparse-matrices"]], "Windows": [[10, "windows"]], "Windows Users": [[5, "windows-users"]], "Word embeddings": [[48, "word-embeddings"], [91, "word-embeddings"]], "Word vectors with spaCy": [[48, "word-vectors-with-spacy"], [91, "word-vectors-with-spacy"]], "Writing a traditional program to predict quiz2 grade": [[13, "writing-a-traditional-program-to-predict-quiz2-grade"], [76, "writing-a-traditional-program-to-predict-quiz2-grade"]], "XGBoost": [[22, "xgboost"], [42, "xgboost"], [85, "xgboost"]], "[Optional] Jupyterlab and Python": [[10, "optional-jupyterlab-and-python"]], "[] notation": [[8, "notation"]], "class_weight=\"balanced\"": [[20, "class-weight-balanced"], [83, "class-weight-balanced"]], "cross_val_score": [[14, "cross-val-score"], [77, "cross-val-score"]], "cross_validate": [[14, "cross-validate"], [77, "cross-validate"]], "fit and transform paradigm for transformers": [[16, "fit-and-transform-paradigm-for-transformers"], [79, "fit-and-transform-paradigm-for-transformers"]], "fit the classifier": [[13, "fit-the-classifier"], [76, "fit-the-classifier"]], "fit, predict , and score summary": [[13, "fit-predict-and-score-summary"], [76, "fit-predict-and-score-summary"]], "iClicker": [[101, "iclicker"]], "iClicker Exercise 10.1": [[21, "iclicker-exercise-10-1"], [84, "iclicker-exercise-10-1"]], "iClicker Exercise 10.2": [[21, "iclicker-exercise-10-2"], [84, "iclicker-exercise-10-2"]], "iClicker Exercise 12.0": [[22, "iclicker-exercise-12-0"], [85, "iclicker-exercise-12-0"]], "iClicker Exercise 12.1": [[22, "iclicker-exercise-12-1"], [42, "iclicker-exercise-12-1"], [85, "iclicker-exercise-12-1"]], "iClicker Exercise 14.1": [[24, "iclicker-exercise-14-1"], [87, "iclicker-exercise-14-1"]], "iClicker Exercise 19.1": [[27, "iclicker-exercise-19-1"], [92, "iclicker-exercise-19-1"]], "iClicker Exercise 2.2 Supervised vs unsupervised": [[13, "iclicker-exercise-2-2-supervised-vs-unsupervised"], [76, "iclicker-exercise-2-2-supervised-vs-unsupervised"]], "iClicker Exercise 2.3 Classification vs regression": [[13, "iclicker-exercise-2-3-classification-vs-regression"], [76, "iclicker-exercise-2-3-classification-vs-regression"]], "iClicker Exercise 2.5: Baselines and decision trees": [[13, "iclicker-exercise-2-5-baselines-and-decision-trees"], [76, "iclicker-exercise-2-5-baselines-and-decision-trees"]], "iClicker Exercise 3.1": [[14, "iclicker-exercise-3-1"], [77, "iclicker-exercise-3-1"]], "iClicker Exercise 3.2": [[14, "iclicker-exercise-3-2"], [77, "iclicker-exercise-3-2"]], "iClicker Exercise 9.1": [[20, "iclicker-exercise-9-1"], [83, "iclicker-exercise-9-1"]], "iClicker Exercise 9.2": [[20, "iclicker-exercise-9-2"], [83, "iclicker-exercise-9-2"]], "iClicker question": [[30, "iclicker-question"]], "k-Nearest Neighbours (k-NNs) [video]": [[15, "k-nearest-neighbours-k-nns-video"], [78, "k-nearest-neighbours-k-nns-video"]], 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Lecture 22: Communication
Lecture 24: Deployment and conclusion
-Final exam review guiding questions
+Final exam preparation: guiding questions
Appendix A: Demo of feature engineering for text data
Appendix B: Multi-class, meta-strategies
sklearn pipelines", "Lecture 6: sklearn ColumnTransformer and Text Features", "Lecture 7: Linear Models", "Lecture 8: Hyperparameter Optimization and Optimization Bias", "Lecture 9: Classification metrics", "Lecture 10: Regression metrics", "Lecture 12: Ensembles", "Lecture 13: Feature importances and model transparency", "Lecture 14: Feature engineering and feature selection", "Lecture 15: K-Means Clustering", "Lecture 16: More Clustering", "Lecture 19: Multi-class classification and introduction to computer vision", "Lecture 20: Time series", "Lecture 21: Survival analysis", "Lecture 22: Communication", "Section 101", "Section 102", "Lecture 3: ML Fundamentals Class Demo", "Lecture 4: Class demo", "Lecture 5 and 6: Class demo", "Lectures 7: Class demo", "Lectures 7: Class demo", "Lectures 8: Class demo", "Lectures 8: Class demo", "Lecture 9: Class demo", "Lecture 10: Class demo", "Lecture 12: Ensembles", "Lecture 13: Class demo", "Lecture 14: Class demo", "Lecture 15: Clustering class demo", "Lecture 15: Class demo", "Lecture 17: Class demo", "Lecture 18: Class demo", "Lecture 20: Class demo", "Lecture 21: Class demo (Based on the lecture notes)", "Lecture 22: Class demo (Based on the lecture notes)", "Lecture 1", "Lecture 10", "Lecture 11", "Lecture 2", "Lecture 3", "Lecture 4", "Lecture 5", "Lecture 6", "Lecture 7", "Lecture 8", "Lecture 9", "Section 103", "Lecture 3 Demo: ML Fundamentals", "Lecture 1", "Lecture 11", "Lecture 2", "Lecture 3", "Lecture 4", "Lecture 5", "Lecture 6", "Lecture 7", "Lecture 8", "Lecture 9", "Lecture 1: Course Introduction", "Lecture 2: Terminology, Baselines, Decision Trees", "Lecture 3: Machine Learning Fundamentals", "Lecture 4: \\(k\\)-Nearest Neighbours and SVM RBFs", "Lecture 5: Preprocessing and sklearn pipelines", "Lecture 6: sklearn ColumnTransformer and Text Features", "Lecture 7: Linear Models", "Lecture 8: Hyperparameter Optimization and Optimization Bias", "Lecture 9: Classification metrics", "Lecture 10: Regression metrics", "Lecture 12: Ensembles", "Lecture 13: Feature importances and model transparency", "Lecture 14: Feature engineering and feature selection", "Lecture 15: K-Means Clustering", "Lecture 16: More Clustering", "Lecture 17: Recommender Systems", "Lecture 18: Introduction to natural language processing", "Lecture 19: Multi-class classification and introduction to computer vision", "Lecture 20: Time series", "Lecture 21: Survival analysis", "Lecture 22: Communication", "Lecture 24: Deployment and conclusion", "Appendix A: Demo of feature engineering for text data", "Appendix B: Multi-class, meta-strategies", "Final exam review guiding questions", "<no title>", "Syllabus"], "titleterms": {"": [12, 14, 15, 16, 17, 20, 21, 23, 28, 30, 35, 40, 41, 43, 45, 49, 51, 64, 75, 77, 78, 79, 80, 83, 84, 86, 93, 95], "0": [22, 85], "1": [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 38, 39, 42, 47, 50, 51, 52, 65, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 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+Search.setIndex({"alltitles": {"": [[33, "id1"]], "(Optional) Changing the data": [[20, "optional-changing-the-data"], [83, "optional-changing-the-data"]], "(Optional) Evaluation": [[29, "optional-evaluation"], [50, "optional-evaluation"], [94, "optional-evaluation"]], "(Optional) Evaluation metrics for multi-class classification": [[20, "optional-evaluation-metrics-for-multi-class-classification"], [83, "optional-evaluation-metrics-for-multi-class-classification"]], "(Optional) Example 1: Optimization bias": [[19, "optional-example-1-optimization-bias"], [82, "optional-example-1-optimization-bias"]], "(Optional) Example 2: Optimization bias": [[19, "optional-example-2-optimization-bias"], [82, "optional-example-2-optimization-bias"]], "(Optional) Fancier methods": [[19, "optional-fancier-methods"], [38, "optional-fancier-methods"], [39, "optional-fancier-methods"], [82, "optional-fancier-methods"]], "(Optional) Fitting in boosted regression trees.": [[22, "optional-fitting-in-boosted-regression-trees"], [42, "optional-fitting-in-boosted-regression-trees"], [85, "optional-fitting-in-boosted-regression-trees"]], "(Optional) Forward or backward selection": [[24, "optional-forward-or-backward-selection"], [87, "optional-forward-or-backward-selection"]], "(Optional) Macro average and weighted average": [[20, "optional-macro-average-and-weighted-average"], [83, "optional-macro-average-and-weighted-average"]], "(Optional) Parametric vs non parametric": [[15, "optional-parametric-vs-non-parametric"], [78, "optional-parametric-vs-non-parametric"]], "(Optional) Passing probability distributions to random search": [[19, "optional-passing-probability-distributions-to-random-search"], [38, "optional-passing-probability-distributions-to-random-search"], [39, "optional-passing-probability-distributions-to-random-search"], [82, "optional-passing-probability-distributions-to-random-search"]], "(Optional) Prediction in boosted regression trees": [[22, "optional-prediction-in-boosted-regression-trees"], [42, "optional-prediction-in-boosted-regression-trees"], [85, "optional-prediction-in-boosted-regression-trees"]], "(Optional) Problems with feature selection": [[24, "optional-problems-with-feature-selection"], [87, "optional-problems-with-feature-selection"]], "(Optional) Search and score": [[24, "optional-search-and-score"], [87, "optional-search-and-score"]], "(Optional) Searching for optimal parameters with successive halving\u00b6": [[19, "optional-searching-for-optimal-parameters-with-successive-halving"], [38, "optional-searching-for-optimal-parameters-with-successive-halving"], [39, "optional-searching-for-optimal-parameters-with-successive-halving"], [82, "optional-searching-for-optimal-parameters-with-successive-halving"]], "(Optional) Setting up a directory structure and environment": [[96, "optional-setting-up-a-directory-structure-and-environment"]], "(Optional) Some more details": [[20, "optional-some-more-details"], [40, "optional-some-more-details"], [83, "optional-some-more-details"]], "(Supervised) machine learning: popular definition": [[12, "supervised-machine-learning-popular-definition"], [75, "supervised-machine-learning-popular-definition"]], "(iClicker) Exercise 14.1": [[24, "id1"], [87, "id1"]], "(iClicker) Exercise 21.1": [[29, "iclicker-exercise-21-1"], [50, "iclicker-exercise-21-1"], [94, "iclicker-exercise-21-1"]], "(iClicker) Exercise 21.2": [[29, "iclicker-exercise-21-2"], [29, "id5"], [50, "iclicker-exercise-21-2"], [94, "iclicker-exercise-21-2"]], "(iClicker) Exercise 3.1": [[15, "iclicker-exercise-3-1"]], "(iClicker) Exercise 3.2": [[15, "iclicker-exercise-3-2"]], "(iClicker) Exercise 4.1": [[78, "iclicker-exercise-4-1"]], "(iClicker) Exercise 4.2": [[78, "iclicker-exercise-4-2"]], "(iClicker) Exercise 5.1": [[16, "iclicker-exercise-5-1"], [79, "iclicker-exercise-5-1"]], "(iClicker) Exercise 5.2": [[16, "iclicker-exercise-5-2"], [79, "iclicker-exercise-5-2"]], "(iClicker) Exercise 5.3": [[16, "iclicker-exercise-5-3"], [79, "iclicker-exercise-5-3"]], "(iClicker) Exercise 6.1": [[17, "iclicker-exercise-6-1"], [80, "iclicker-exercise-6-1"]], "(iClicker) Exercise 6.2": [[17, "iclicker-exercise-6-2"], [80, "iclicker-exercise-6-2"]], "(iClicker) Exercise 7.1": [[18, "iclicker-exercise-7-1"], [81, "iclicker-exercise-7-1"]], "(iClicker) Exercise 7.2": [[18, "iclicker-exercise-7-2"], [81, "iclicker-exercise-7-2"]], "(iClicker) Exercise 8.1": [[19, "iclicker-exercise-8-1"], [82, "iclicker-exercise-8-1"]], "(iClicker) Midterm poll": [[25, "iclicker-midterm-poll"], [88, "iclicker-midterm-poll"]], "15.1 Select all of the following statements which are True (iClicker)": [[25, "select-all-of-the-following-statements-which-are-true-iclicker"], [88, "select-all-of-the-following-statements-which-are-true-iclicker"]], "15.2 Select all of the following statements which are True (iClicker)": [[25, "id1"], [88, "id1"]], "15.3 Select all of the following statements which are 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[[15, "analogy-based-algorithms-in-practice"], [78, "analogy-based-algorithms-in-practice"]], "Analogy-based models": [[15, "analogy-based-models"], [78, "analogy-based-models"]], "Announcements": [[81, "announcements"]], "Appendix A: Demo of feature engineering for text data": [[97, null]], "Appendix B: Multi-class, meta-strategies": [[98, null]], "Applying feature transformations": [[21, "applying-feature-transformations"], [30, "applying-feature-transformations"], [41, "applying-feature-transformations"], [51, "applying-feature-transformations"], [84, "applying-feature-transformations"], [95, "applying-feature-transformations"]], "Applying functions to a dataframe with df.apply() and df.applymap()": [[8, "applying-functions-to-a-dataframe-with-df-apply-and-df-applymap"]], "Approach 1: Only consider the examples where \u201cChurn\u201d=Yes": [[29, "approach-1-only-consider-the-examples-where-churn-yes"], [50, "approach-1-only-consider-the-examples-where-churn-yes"], [94, 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"Attention": [[13, null], [13, null], [13, null], [15, null], [76, null], [76, null], [76, null], [78, null]], "Attribution": [[30, "attribution"], [51, "attribution"], [95, "attribution"]], "Automated hyperparameter optimization": [[19, "automated-hyperparameter-optimization"], [19, "id3"], [82, "automated-hyperparameter-optimization"], [82, "id3"]], "Averaging": [[22, "averaging"], [42, "averaging"], [85, "averaging"]], "Bad range for hyperparameters": [[19, "bad-range-for-hyperparameters"], [38, "bad-range-for-hyperparameters"], [39, "bad-range-for-hyperparameters"], [82, "bad-range-for-hyperparameters"]], "Bag of words (BOW) representation": [[17, "bag-of-words-bow-representation"], [80, "bag-of-words-bow-representation"]], "Bag-of-words model": [[97, "bag-of-words-model"]], "Baseline": [[20, "baseline"], [23, "baseline"], [40, "baseline"], [43, "baseline"], [83, "baseline"], [86, "baseline"]], "Baseline Approaches": [[47, "baseline-approaches"], [90, "baseline-approaches"]], "Baseline model": [[33, "baseline-model"], [64, "baseline-model"]], "Baselines": [[13, "baselines"], [22, "baselines"], [42, "baselines"], [76, "baselines"], [85, "baselines"]], "Baselines [video]": [[13, "baselines-video"], [76, "baselines-video"]], "Basic text preprocessing [video]": [[48, "basic-text-preprocessing-video"], [91, "basic-text-preprocessing-video"]], "Better features usually help more than a better model.": [[24, "better-features-usually-help-more-than-a-better-model"], [87, "better-features-usually-help-more-than-a-better-model"]], "Beyond error rate in recommendation systems": [[47, "beyond-error-rate-in-recommendation-systems"], [90, "beyond-error-rate-in-recommendation-systems"]], "Bias vs variance tradeoff": [[14, "bias-vs-variance-tradeoff"], [77, "bias-vs-variance-tradeoff"]], "Big picture and datasets": [[13, "big-picture-and-datasets"], [76, "big-picture-and-datasets"]], "Big picture and motivation": [[14, "big-picture-and-motivation"], [77, 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"building-a-supervise-machine-learning-model"]], "Building and deploying a web app": [[96, "building-and-deploying-a-web-app"]], "Building decision trees with sklearn": [[13, "building-decision-trees-with-sklearn"], [76, "building-decision-trees-with-sklearn"]], "Building user profiles": [[47, "building-user-profiles"], [90, "building-user-profiles"]], "CPSC 330 Documents": [[3, null]], "CPSC 330 Python notes": [[8, null]], "CPSC 330 grading policies": [[6, null]], "CPSC 330 vs. 340": [[12, "cpsc-330-vs-340"]], "CPSC 330 vs. CPSC 340": [[2, null]], "Can we learn without targets?": [[25, "can-we-learn-without-targets"], [88, "can-we-learn-without-targets"]], "Can we use this feature in the model?": [[16, "can-we-use-this-feature-in-the-model"], [79, "can-we-use-this-feature-in-the-model"]], "Cases where it\u2019s OK to break the golden rule": [[17, "cases-where-it-s-ok-to-break-the-golden-rule"], [80, "cases-where-it-s-ok-to-break-the-golden-rule"]], "CatBoost": [[22, "catboost"], [42, "catboost"], [85, "catboost"]], "Categorical features": [[23, "categorical-features"], [35, "categorical-features"], [43, "categorical-features"], [86, "categorical-features"]], "Categorical features [video]": [[16, "categorical-features-video"], [79, "categorical-features-video"]], "Categorical features with only two possible categories": [[17, "categorical-features-with-only-two-possible-categories"], [80, "categorical-features-with-only-two-possible-categories"]], "Censoring and survival analysis": [[29, "censoring-and-survival-analysis"], [50, "censoring-and-survival-analysis"], [94, "censoring-and-survival-analysis"]], "Centre for Accessibility (CfA) Exam Accommodations": [[101, "centre-for-accessibility-cfa-exam-accommodations"]], "Changing the training procedure": [[20, "changing-the-training-procedure"], [83, "changing-the-training-procedure"]], "Characters in this course?": [[12, "characters-in-this-course"], [75, "characters-in-this-course"]], "Checklist for you before next class": [[12, "checklist-for-you-before-next-class"]], "Choosing K [video]": [[25, "choosing-k-video"], [88, "choosing-k-video"]], "Choosing n_neighbors": [[15, "choosing-n-neighbors"], [78, "choosing-n-neighbors"]], "Citing sources": [[7, "citing-sources"]], "Class imbalance in training sets": [[20, "class-imbalance-in-training-sets"], [83, "class-imbalance-in-training-sets"]], "Class meetings": [[101, "class-meetings"]], "Classification report": [[20, "classification-report"], [40, "classification-report"], [83, "classification-report"]], "Classification vs. Regression": [[13, "classification-vs-regression"], [76, "classification-vs-regression"]], "Classification with KNeighborsClassifier": [[34, "classification-with-kneighborsclassifier"]], "Clustering": [[99, "clustering"]], "Clustering Activity (~5 mins)": [[25, "clustering-activity-5-mins"], [88, "clustering-activity-5-mins"]], "Clustering motivation [video]": [[25, "clustering-motivation-video"], [88, "clustering-motivation-video"]], "Clustering: Input and (possible) output": [[25, "clustering-input-and-possible-output"], [88, "clustering-input-and-possible-output"]], "Code of conduct": [[101, "code-of-conduct"]], "Coefficients and intercept": [[18, "coefficients-and-intercept"], [81, "coefficients-and-intercept"]], "ColumnTransformer example": [[17, "columntransformer-example"], [80, "columntransformer-example"]], "ColumnTransformer on the California housing dataset": [[17, "columntransformer-on-the-california-housing-dataset"], [80, "columntransformer-on-the-california-housing-dataset"]], "ColumnTransformer: Transformed data": [[17, "columntransformer-transformed-data"], [80, "columntransformer-transformed-data"]], "Coming up \u2026": [[14, "coming-up"], [77, "coming-up"]], "Coming up:": [[15, "coming-up"], [78, "coming-up"]], "Command-line git": [[5, "command-line-git"]], "Common applications": [[25, "common-applications"], [88, "common-applications"]], "Common preprocessing techniques": [[16, "common-preprocessing-techniques"], [79, "common-preprocessing-techniques"]], "Communication": [[99, "communication"]], "Communications": [[12, "communications"]], "Completing the utility matrix with content-based filtering": [[47, "completing-the-utility-matrix-with-content-based-filtering"], [90, "completing-the-utility-matrix-with-content-based-filtering"]], "Components of a linear classifier": [[18, "components-of-a-linear-classifier"], [81, "components-of-a-linear-classifier"]], "Concepts then labels, not the other way around": [[30, "concepts-then-labels-not-the-other-way-around"], [51, "concepts-then-labels-not-the-other-way-around"], [95, "concepts-then-labels-not-the-other-way-around"]], "Concepts we revised in this demo": [[33, "concepts-we-revised-in-this-demo"], [64, "concepts-we-revised-in-this-demo"]], "Conclusion & farewell": [[96, "conclusion-farewell"]], "Confidence and predict_proba (20 min)": [[30, "confidence-and-predict-proba-20-min"], [51, "confidence-and-predict-proba-20-min"], [95, "confidence-and-predict-proba-20-min"]], "Confusing and perhaps misleading visualization of results": [[30, "confusing-and-perhaps-misleading-visualization-of-results"], [51, "confusing-and-perhaps-misleading-visualization-of-results"], [95, "confusing-and-perhaps-misleading-visualization-of-results"]], "Confusion matrix": [[40, "confusion-matrix"], [83, "confusion-matrix"]], "Confusion matrix (video)": [[20, "confusion-matrix-video"]], "Confusion matrix with cross-validation": [[20, "confusion-matrix-with-cross-validation"], [40, "confusion-matrix-with-cross-validation"], [83, "confusion-matrix-with-cross-validation"]], "Cons of k-NNs for supervised learning": [[15, "cons-of-k-nns-for-supervised-learning"], [78, "cons-of-k-nns-for-supervised-learning"]], "Content-based filtering": [[47, "content-based-filtering"], [90, "content-based-filtering"]], "Convenient make_column_transformer syntax": [[17, "convenient-make-column-transformer-syntax"], [80, "convenient-make-column-transformer-syntax"]], "Course Learning Objectives": [[11, null]], "Course co-ordinator": [[1, "course-co-ordinator"], [101, "course-co-ordinator"]], "Course description": [[101, "course-description"]], "Course format": [[12, "course-format"]], "Course review / conclusion (~20 min)": [[96, "course-review-conclusion-20-min"]], "Course website": [[12, "course-website"]], "Cox proportional hazards model": [[29, "cox-proportional-hazards-model"], [50, "cox-proportional-hazards-model"], [94, "cox-proportional-hazards-model"]], "Create X and y": [[13, "create-x-and-y"], [76, "create-x-and-y"]], "Create a classifier object": [[13, "create-a-classifier-object"], [76, "create-a-classifier-object"]], "Create a column transformer": [[17, "create-a-column-transformer"], [80, "create-a-column-transformer"]], "Creating train_df and test_df": [[14, "creating-train-df-and-test-df"], [77, "creating-train-df-and-test-df"]], "Creating utility matrix": [[47, "creating-utility-matrix"], [90, "creating-utility-matrix"]], "Credit": [[10, "credit"]], "Cross validation with different metrics": [[20, "cross-validation-with-different-metrics"], [40, "cross-validation-with-different-metrics"], [83, "cross-validation-with-different-metrics"]], "Cross-validation": [[28, "cross-validation"], [28, "id4"], [33, "cross-validation"], [49, "cross-validation"], [49, "id5"], [64, "cross-validation"], [93, "cross-validation"], [93, "id4"]], "Cross-validation [video]": [[14, "cross-validation-video"], [77, "cross-validation-video"]], "Cross-validation to the rescue!!": [[14, "cross-validation-to-the-rescue"], [77, "cross-validation-to-the-rescue"]], "Cross-validation using scikit-learn": [[14, "cross-validation-using-scikit-learn"], [77, "cross-validation-using-scikit-learn"]], "Curse of dimensionality": [[15, "curse-of-dimensionality"], [78, "curse-of-dimensionality"]], "Customer churn": [[29, "customer-churn"], [50, "customer-churn"], [94, "customer-churn"]], "Customer segmentation": [[25, "customer-segmentation"], [88, "customer-segmentation"]], "DBSCAN": [[46, "dbscan"]], "DBSCAN [video]": [[26, "dbscan-video"], [89, "dbscan-video"]], "DBSCAN introduction": [[26, "dbscan-introduction"], [89, "dbscan-introduction"]], "DBSCAN: failure cases": [[26, "dbscan-failure-cases"], [26, "id1"], [89, "dbscan-failure-cases"], [89, "id1"]], "Data": [[17, "data"], [18, "data"], [22, "data"], [23, "data"], [23, "id1"], [42, "data"], [43, "data"], [43, "id1"], [80, "data"], [81, "data"], [85, "data"], [86, "data"], [86, "id1"]], "Data Splitting [video]": [[14, "data-splitting-video"], [77, "data-splitting-video"]], "Data and main approaches": [[47, "data-and-main-approaches"], [90, "data-and-main-approaches"]], "Data and splitting": [[35, "data-and-splitting"]], "Data exploration": [[25, "data-exploration"], [88, "data-exploration"]], "Data splitting": [[33, "data-splitting"], [64, "data-splitting"]], "Dataframe summaries": [[8, "dataframe-summaries"]], "Dataset": [[27, "dataset"], [30, "dataset"], [51, "dataset"], [92, "dataset"], [95, "dataset"]], "Dataset [video]": [[21, "dataset-video"], [41, "dataset-video"], [84, "dataset-video"]], "Dataset for demonstration": [[20, "dataset-for-demonstration"], [40, "dataset-for-demonstration"], [83, "dataset-for-demonstration"]], "Dataset, splitting, and baseline": [[16, "dataset-splitting-and-baseline"], [79, "dataset-splitting-and-baseline"]], "Datasets": [[7, "datasets"]], "Dealing with class imbalance [video]": [[20, "dealing-with-class-imbalance-video"], [83, "dealing-with-class-imbalance-video"]], "Dealing with unknown categories": [[17, "dealing-with-unknown-categories"], [80, "dealing-with-unknown-categories"]], "Debugging": [[10, "debugging"]], "Decision boundaries playground": [[34, "decision-boundaries-playground"]], "Decision boundary": [[13, "decision-boundary"], [76, "decision-boundary"]], "Decision boundary for max_depth=1": [[13, "decision-boundary-for-max-depth-1"], [76, "decision-boundary-for-max-depth-1"]], "Decision boundary for max_depth=2": [[13, "decision-boundary-for-max-depth-2"], [76, "decision-boundary-for-max-depth-2"]], "Decision boundary for max_depth=5": [[13, "decision-boundary-for-max-depth-5"], [76, "decision-boundary-for-max-depth-5"]], "Decision boundary of SVMs": [[15, "decision-boundary-of-svms"], [78, "decision-boundary-of-svms"]], "Decision boundary of logistic regression": [[18, "decision-boundary-of-logistic-regression"], [81, "decision-boundary-of-logistic-regression"]], "Decision tree algorithm": [[13, "decision-tree-algorithm"], [76, "decision-tree-algorithm"]], "Decision tree feature importances": [[23, "decision-tree-feature-importances"], [43, "decision-tree-feature-importances"], [86, "decision-tree-feature-importances"]], "Decision tree for regression problems": [[13, "decision-tree-for-regression-problems"], [76, "decision-tree-for-regression-problems"]], "Decision tree model": [[33, "decision-tree-model"], [64, "decision-tree-model"]], "Decision tree with max_depth=1": [[13, "decision-tree-with-max-depth-1"], [76, "decision-tree-with-max-depth-1"]], "Decision tree with max_depth=3": [[13, "decision-tree-with-max-depth-3"], [76, "decision-tree-with-max-depth-3"]], "Decision trees [video]": [[13, "decision-trees-video"], [76, "decision-trees-video"]], "Decision trees with continuous features": [[13, "decision-trees-with-continuous-features"], [76, "decision-trees-with-continuous-features"]], "DecisionTreeClassifier baseline": [[22, "decisiontreeclassifier-baseline"], [42, "decisiontreeclassifier-baseline"], [85, "decisiontreeclassifier-baseline"]], "DecisionTreeClassifier on quiz2 grade prediction toy dataset": [[13, "decisiontreeclassifier-on-quiz2-grade-prediction-toy-dataset"], [76, "decisiontreeclassifier-on-quiz2-grade-prediction-toy-dataset"]], "Decisions involve a few key pieces": [[30, "decisions-involve-a-few-key-pieces"], [51, "decisions-involve-a-few-key-pieces"], [95, "decisions-involve-a-few-key-pieces"]], "Decreasing the threshold": [[20, "decreasing-the-threshold"], [40, "decreasing-the-threshold"], [83, "decreasing-the-threshold"]], "Deep learning": [[28, "deep-learning"], [49, "deep-learning"], [93, "deep-learning"]], "Deep learning software": [[27, "deep-learning-software"], [92, "deep-learning-software"]], "Deliverable due dates (tentative)": [[1, "deliverable-due-dates-tentative"]], "Demo of creating a new web service": [[96, "demo-of-creating-a-new-web-service"]], "Demo of feature engineering with numeric features": [[24, "demo-of-feature-engineering-with-numeric-features"], [44, "demo-of-feature-engineering-with-numeric-features"], [87, "demo-of-feature-engineering-with-numeric-features"]], "Demo: A more complicated dataset": [[28, "demo-a-more-complicated-dataset"], [49, "demo-a-more-complicated-dataset"], [93, "demo-a-more-complicated-dataset"]], "Demo: Deploying moment classification model": [[96, "demo-deploying-moment-classification-model"]], "Demo: Model interpretation of linear classifiers": [[36, "demo-model-interpretation-of-linear-classifiers"], [37, "demo-model-interpretation-of-linear-classifiers"]], "Dendrogram": [[26, "dendrogram"], [89, "dendrogram"]], "Deploying the API on a server": [[96, "deploying-the-api-on-a-server"]], "Deployment": [[99, "deployment"]], "Difference between Statistics and Machine Learning": [[96, "difference-between-statistics-and-machine-learning"]], "Different models": [[23, "different-models"], [43, "different-models"], [86, "different-models"]], "Different range for hyperparameters yields better results!": [[19, "different-range-for-hyperparameters-yields-better-results"], [38, "different-range-for-hyperparameters-yields-better-results"], [39, "different-range-for-hyperparameters-yields-better-results"], [82, "different-range-for-hyperparameters-yields-better-results"]], "Different scoring functions with cross_validate": [[21, "different-scoring-functions-with-cross-validate"], [41, "different-scoring-functions-with-cross-validate"], [84, "different-scoring-functions-with-cross-validate"]], "Dimensions in ML problems": [[15, "dimensions-in-ml-problems"], [78, "dimensions-in-ml-problems"]], "Discuss the following questions in your group": [[33, "discuss-the-following-questions-in-your-group"], [64, "discuss-the-following-questions-in-your-group"]], "Discussion": [[96, "discussion"]], "Discussion question": [[48, "discussion-question"], [91, "discussion-question"]], "Discussion questions": [[35, "discussion-questions"]], "Discussion questions:": [[30, "discussion-questions"], [51, "discussion-questions"], [95, "discussion-questions"]], "Distance between feature vectors": [[15, "distance-between-feature-vectors"], [78, "distance-between-feature-vectors"]], "Do we actually want to use certain features for prediction?": [[17, "do-we-actually-want-to-use-certain-features-for-prediction"], [80, "do-we-actually-want-to-use-certain-features-for-prediction"]], "Do we have class imbalance?": [[22, "do-we-have-class-imbalance"], [23, "do-we-have-class-imbalance"], [42, "do-we-have-class-imbalance"], [43, "do-we-have-class-imbalance"], [85, "do-we-have-class-imbalance"], [86, "do-we-have-class-imbalance"]], "Do we have correlated features?": [[23, "do-we-have-correlated-features"], [43, "do-we-have-correlated-features"], [86, "do-we-have-correlated-features"]], "Document clustering": [[25, "document-clustering"], [88, "document-clustering"]], "Domain-specific transformations": [[24, "domain-specific-transformations"], [87, "domain-specific-transformations"]], "Dummy Classifier": [[35, "dummy-classifier"]], "Dummy classifier": [[97, "dummy-classifier"]], "Dummy model": [[34, "dummy-model"]], "DummyClassifier": [[13, "dummyclassifier"], [28, "dummyclassifier"], [29, "dummyclassifier"], [49, "dummyclassifier"], [50, "dummyclassifier"], [76, "dummyclassifier"], [93, "dummyclassifier"], [94, "dummyclassifier"]], "DummyClassifier baseline": [[22, "dummyclassifier-baseline"], [42, "dummyclassifier-baseline"], [85, "dummyclassifier-baseline"]], "DummyClassifier on quiz2 grade prediction toy dataset": [[13, "dummyclassifier-on-quiz2-grade-prediction-toy-dataset"], [76, "dummyclassifier-on-quiz2-grade-prediction-toy-dataset"]], "DummyRegressor": [[13, "dummyregressor"], [21, "dummyregressor"], [41, "dummyregressor"], [76, "dummyregressor"], [84, "dummyregressor"]], "EDA": [[16, "eda"], [20, "eda"], [21, "eda"], [40, "eda"], [41, "eda"], [79, "eda"], [83, "eda"], [84, "eda"]], "Encoding text data": [[80, "encoding-text-data"]], "Encoding text data (Video)": [[17, "encoding-text-data-video"]], "Encoding time as a number": [[28, "encoding-time-as-a-number"], [49, "encoding-time-as-a-number"], [93, "encoding-time-as-a-number"]], "Encoding time of day as a categorical feature": [[28, "encoding-time-of-day-as-a-categorical-feature"], [49, "encoding-time-of-day-as-a-categorical-feature"], [93, "encoding-time-of-day-as-a-categorical-feature"]], "Ensembles": [[99, "ensembles"]], "Equally good": [[30, "equally-good"], [51, "equally-good"], [95, "equally-good"]], "Ethics": [[99, "ethics"]], "Euclidean distance": [[15, "euclidean-distance"], [78, "euclidean-distance"]], "Evaluating DBSCAN clusters": [[26, "evaluating-dbscan-clusters"], [89, "evaluating-dbscan-clusters"]], "Evaluation": [[47, "evaluation"], [47, "id3"], [90, "evaluation"], [90, "id3"]], "Evaluation metrics": [[99, "evaluation-metrics"]], "Evaluation metrics for binary classification: Motivation": [[20, "evaluation-metrics-for-binary-classification-motivation"], [40, "evaluation-metrics-for-binary-classification-motivation"], [83, "evaluation-metrics-for-binary-classification-motivation"]], "Evalution metrics overview": [[20, "evalution-metrics-overview"], [40, "evalution-metrics-overview"], [83, "evalution-metrics-overview"]], "Examining learned coefficients": [[36, "examining-learned-coefficients"], [37, "examining-learned-coefficients"]], "Examining the preprocessed data": [[21, "examining-the-preprocessed-data"], [30, "examining-the-preprocessed-data"], [41, "examining-the-preprocessed-data"], [51, "examining-the-preprocessed-data"], [84, "examining-the-preprocessed-data"], [95, "examining-the-preprocessed-data"]], "Examining the vocabulary": [[36, "examining-the-vocabulary"], [37, "examining-the-vocabulary"]], "Example": [[18, "example"], [22, "example"], [42, "example"], [81, "example"], [85, "example"]], "Example 1": [[23, "example-1"]], "Example 1: Predicting whether a patient has a liver disease or not": [[12, "example-1-predicting-whether-a-patient-has-a-liver-disease-or-not"], [75, "example-1-predicting-whether-a-patient-has-a-liver-disease-or-not"]], "Example 1: What is \u201ccorrect\u201d grouping?": [[25, "example-1-what-is-correct-grouping"], [88, "example-1-what-is-correct-grouping"]], "Example 1: quiz 2 grade prediction": [[13, "example-1-quiz-2-grade-prediction"], [76, "example-1-quiz-2-grade-prediction"]], "Example 2": [[23, "example-2"]], "Example 2: Predicting country using the longitude and latitude": [[13, "example-2-predicting-country-using-the-longitude-and-latitude"], [76, "example-2-predicting-country-using-the-longitude-and-latitude"]], "Example 2: Predicting the label of a given image": [[12, "example-2-predicting-the-label-of-a-given-image"], [75, "example-2-predicting-the-label-of-a-given-image"]], "Example 3: Predicting sentiment expressed in a movie review": [[12, "example-3-predicting-sentiment-expressed-in-a-movie-review"], [75, "example-3-predicting-sentiment-expressed-in-a-movie-review"]], "Example 4: Predicting housing prices": [[12, "example-4-predicting-housing-prices"], [75, "example-4-predicting-housing-prices"]], "Example showing how can we interpret coefficients of scaled features.": [[23, "example-showing-how-can-we-interpret-coefficients-of-scaled-features"], [43, "example-showing-how-can-we-interpret-coefficients-of-scaled-features"], [86, "example-showing-how-can-we-interpret-coefficients-of-scaled-features"]], "Example: Is \u201cRelevance\u201d clearly defined?": [[24, "example-is-relevance-clearly-defined"], [87, "example-is-relevance-clearly-defined"]], "Example: Predict whether a message is spam or not": [[12, "example-predict-whether-a-message-is-spam-or-not"], [75, "example-predict-whether-a-message-is-spam-or-not"]], "Example: Supervised vs unsupervised learning": [[25, "example-supervised-vs-unsupervised-learning"], [88, "example-supervised-vs-unsupervised-learning"]], "Example: Tabular data for grade prediction": [[13, "example-tabular-data-for-grade-prediction"], [76, "example-tabular-data-for-grade-prediction"]], "Example: Tabular data for the housing price prediction": [[13, "example-tabular-data-for-the-housing-price-prediction"], [76, "example-tabular-data-for-the-housing-price-prediction"]], "Example: class_weight parameter of sklearn LogisticRegression": [[20, "example-class-weight-parameter-of-sklearn-logisticregression"], [83, "example-class-weight-parameter-of-sklearn-logisticregression"]], "Example: k-nearest neighbours on the Spotify dataset": [[16, "example-k-nearest-neighbours-on-the-spotify-dataset"], [79, "example-k-nearest-neighbours-on-the-spotify-dataset"]], "Examples": [[12, "examples"], [75, "examples"]], "Exercise 17.1 Select all of the following statements which are True (iClicker)": [[47, "exercise-17-1-select-all-of-the-following-statements-which-are-true-iclicker"], [47, "id2"], [90, "exercise-17-1-select-all-of-the-following-statements-which-are-true-iclicker"]], "Exercise 17.2 Select all of the following statements which are True (iClicker)": [[90, "exercise-17-2-select-all-of-the-following-statements-which-are-true-iclicker"]], "Exercise 2.1 Select all of the following statements which are examples of supervised machine learning": [[13, "exercise-2-1-select-all-of-the-following-statements-which-are-examples-of-supervised-machine-learning"], [76, "exercise-2-1-select-all-of-the-following-statements-which-are-examples-of-supervised-machine-learning"]], "Exercise 2.4": [[13, "exercise-2-4"], [76, "exercise-2-4"]], "Exercise 8.2": [[19, "exercise-8-2"], [82, "exercise-8-2"]], "Exhaustive grid search: sklearn.model_selection.GridSearchCV": [[19, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"], [38, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"], [39, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"], [82, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"]], "Explaining a prediction": [[23, "explaining-a-prediction"], [43, "explaining-a-prediction"], [86, "explaining-a-prediction"]], "Explanation 1": [[30, "explanation-1"], [51, "explanation-1"], [95, "explanation-1"]], "Explanation 2": [[30, "explanation-2"], [51, "explanation-2"], [95, "explanation-2"]], "Exploratory Data Analysis": [[33, "exploratory-data-analysis"], [64, "exploratory-data-analysis"]], "Exploratory data analysis": [[28, "exploratory-data-analysis"], [35, "exploratory-data-analysis"], [49, "exploratory-data-analysis"], [93, "exploratory-data-analysis"]], "Extracting BOW features using scikit-learn": [[17, "extracting-bow-features-using-scikit-learn"], [80, "extracting-bow-features-using-scikit-learn"]], "Extracting date and time information": [[28, "extracting-date-and-time-information"], [49, "extracting-date-and-time-information"], [93, "extracting-date-and-time-information"]], "F1-score": [[20, "f1-score"], [40, "f1-score"], [83, "f1-score"]], "Faster method: vectorize the loop over rows": [[8, "faster-method-vectorize-the-loop-over-rows"]], "Fastest method: broadcasting": [[8, "fastest-method-broadcasting"]], "Feature crosses for one-hot encoded features": [[24, "feature-crosses-for-one-hot-encoded-features"], [44, "feature-crosses-for-one-hot-encoded-features"], [87, "feature-crosses-for-one-hot-encoded-features"]], "Feature engineering": [[28, "feature-engineering"], [49, "feature-engineering"], [93, "feature-engineering"]], "Feature engineering and selection": [[99, "feature-engineering-and-selection"]], "Feature engineering for date/time columns": [[28, "feature-engineering-for-date-time-columns"], [49, "feature-engineering-for-date-time-columns"], [93, "feature-engineering-for-date-time-columns"]], "Feature engineering: Encoding date/time as feature(s)": [[28, "feature-engineering-encoding-date-time-as-feature-s"], [49, "feature-engineering-encoding-date-time-as-feature-s"], [93, "feature-engineering-encoding-date-time-as-feature-s"]], "Feature engineering: Motivation": [[24, "feature-engineering-motivation"], [87, "feature-engineering-motivation"]], "Feature importances": [[23, "feature-importances"], [43, "feature-importances"], [86, "feature-importances"], [99, "feature-importances"]], "Feature importances in linear models": [[23, "feature-importances-in-linear-models"], [23, "id2"], [43, "feature-importances-in-linear-models"], [43, "id2"], [86, "feature-importances-in-linear-models"], [86, "id2"]], "Feature interactions and feature crosses": [[24, "feature-interactions-and-feature-crosses"], [44, "feature-interactions-and-feature-crosses"], [87, "feature-interactions-and-feature-crosses"]], "Feature names of transformed data": [[21, "feature-names-of-transformed-data"], [41, "feature-names-of-transformed-data"], [84, "feature-names-of-transformed-data"]], "Feature selection: Introduction and motivation": [[24, "feature-selection-introduction-and-motivation"], [44, "feature-selection-introduction-and-motivation"], [87, "feature-selection-introduction-and-motivation"]], "Feature transformations and the golden rule": [[16, "feature-transformations-and-the-golden-rule"], [79, "feature-transformations-and-the-golden-rule"]], "Feature types": [[21, "feature-types"], [21, "id1"], [30, "feature-types"], [41, "feature-types"], [41, "id1"], [51, "feature-types"], [84, "feature-types"], [84, "id1"], [95, "feature-types"]], "Feature vectors": [[15, "feature-vectors"], [78, "feature-vectors"]], "Figures": [[7, "figures"]], "Filtering a dataframe with [] and df.query()": [[8, "filtering-a-dataframe-with-and-df-query"]], "Final comments and summary": [[19, "final-comments-and-summary"], [47, "final-comments-and-summary"], [82, "final-comments-and-summary"], [90, "final-comments-and-summary"]], "Final comments, summary, and reflection": [[13, "final-comments-summary-and-reflection"], [25, "final-comments-summary-and-reflection"], [26, "final-comments-summary-and-reflection"], [76, "final-comments-summary-and-reflection"], [88, "final-comments-summary-and-reflection"], [89, "final-comments-summary-and-reflection"]], "Final exam": [[101, "final-exam"]], "Final exam preparation: guiding questions": [[99, null]], "Final remarks": [[28, "final-remarks"], [49, "final-remarks"], [93, "final-remarks"]], 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"Generalizing to more features": [[18, "generalizing-to-more-features"], [81, "generalizing-to-more-features"]], "Generalizing to unseen data": [[14, "generalizing-to-unseen-data"], [77, "generalizing-to-unseen-data"]], "Geometric view of tabular data and dimensions": [[15, "geometric-view-of-tabular-data-and-dimensions"], [78, "geometric-view-of-tabular-data-and-dimensions"]], "Git": [[10, "git"]], "GitHub Desktop": [[5, "github-desktop"]], "Global average baseline": [[47, "global-average-baseline"], [90, "global-average-baseline"]], "Golden rule violation: Example 1": [[14, "golden-rule-violation-example-1"], [77, "golden-rule-violation-example-1"]], "Golden rule violation: Example 2": [[14, "golden-rule-violation-example-2"], [77, "golden-rule-violation-example-2"]], "Grades": [[12, "grades"]], "Gradient boosted trees [video]": [[22, "gradient-boosted-trees-video"], [42, "gradient-boosted-trees-video"], [85, "gradient-boosted-trees-video"]], "Gradient boosting in sklearn": [[22, "gradient-boosting-in-sklearn"], [42, "gradient-boosting-in-sklearn"], [85, "gradient-boosting-in-sklearn"]], "Grading concerns: time limit": [[6, "grading-concerns-time-limit"]], "Grading scheme": [[101, "grading-scheme"]], "Grading-related questions": [[4, "grading-related-questions"]], "Handling imbalance": [[20, "handling-imbalance"], [83, "handling-imbalance"]], "Here is the workflow we\u2019ll generally follow.": [[14, "here-is-the-workflow-we-ll-generally-follow"], [77, "here-is-the-workflow-we-ll-generally-follow"]], "Hierarchical clustering": [[46, "hierarchical-clustering"]], "Hierarchical clustering [video]": [[26, "hierarchical-clustering-video"], [89, "hierarchical-clustering-video"]], "Homework info & submission guidelines": [[7, null]], "How are we making predictions?": [[18, "how-are-we-making-predictions"], [81, "how-are-we-making-predictions"]], "How can we avoid violating golden rule?": [[14, "how-can-we-avoid-violating-golden-rule"], [77, "how-can-we-avoid-violating-golden-rule"]], "How can we get feature importances for non sklearn models?": [[23, "how-can-we-get-feature-importances-for-non-sklearn-models"], [43, "how-can-we-get-feature-importances-for-non-sklearn-models"], [86, "how-can-we-get-feature-importances-for-non-sklearn-models"]], "How do they work?": [[22, "how-do-they-work"], [42, "how-do-they-work"], [85, "how-do-they-work"]], "How do we carry out feature selection?": [[24, "how-do-we-carry-out-feature-selection"], [44, "how-do-we-carry-out-feature-selection"], [87, "how-do-we-carry-out-feature-selection"]], "How does fit work?": [[13, "how-does-fit-work"], [13, "id2"], [76, "how-does-fit-work"], [76, "id2"]], "How does it work?": [[26, "how-does-it-work"], [30, "how-does-it-work"], [51, "how-does-it-work"], [89, "how-does-it-work"], [95, "how-does-it-work"]], "How does logistic regression calculate these probabilities?": [[18, "how-does-logistic-regression-calculate-these-probabilities"], [81, "how-does-logistic-regression-calculate-these-probabilities"]], "How does predict work?": [[13, "how-does-predict-work"], [76, "how-does-predict-work"]], "How to approximate generalization error?": [[14, "how-to-approximate-generalization-error"], [77, "how-to-approximate-generalization-error"]], "How to ask for help": [[4, null]], "How to carry out cross-validation?": [[16, "how-to-carry-out-cross-validation"], [79, "how-to-carry-out-cross-validation"]], "How to choose n_neighbors?": [[15, "how-to-choose-n-neighbors"], [78, "how-to-choose-n-neighbors"]], "How to pick a model that would generalize better?": [[14, "how-to-pick-a-model-that-would-generalize-better"], [77, "how-to-pick-a-model-that-would-generalize-better"]], "How to submit": [[7, "how-to-submit"]], "How would you do it properly? 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"Imputation": [[16, "imputation"], [79, "imputation"]], "Imputation and scaling [video]": [[16, "imputation-and-scaling-video"], [79, "imputation-and-scaling-video"]], "Incorporating ordinal feature class_attendance": [[17, "incorporating-ordinal-feature-class-attendance"], [80, "incorporating-ordinal-feature-class-attendance"]], "Incorporating text features": [[35, "incorporating-text-features"]], "Increasing the threshold": [[20, "increasing-the-threshold"], [40, "increasing-the-threshold"], [83, "increasing-the-threshold"]], "Indexing Dataframes": [[8, "indexing-dataframes"]], "Indexing cheatsheet": [[8, "indexing-cheatsheet"]], "Inertia": [[25, "inertia"], [88, "inertia"]], "Initial analysis, EDA, preprocessing": [[96, "initial-analysis-eda-preprocessing"]], "Initialization of K-Means": [[25, "initialization-of-k-means"], [88, "initialization-of-k-means"]], "Inject randomness in the classifier construction": [[22, "inject-randomness-in-the-classifier-construction"], [42, 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"interim-summary"], [87, "interim-summary"], [93, "interim-summary"]], "Interpretation of coefficients": [[18, "interpretation-of-coefficients"], [81, "interpretation-of-coefficients"]], "Interpretation of coefficients in linear models": [[18, "interpretation-of-coefficients-in-linear-models"], [81, "interpretation-of-coefficients-in-linear-models"]], "Interpreting coefficients of numeric features": [[23, "interpreting-coefficients-of-numeric-features"], [43, "interpreting-coefficients-of-numeric-features"], [86, "interpreting-coefficients-of-numeric-features"]], "Introduction": [[26, "introduction"], [89, "introduction"], [99, "introduction"]], "Introduction to NLP": [[99, "introduction-to-nlp"]], "Introduction to computer vision": [[27, "introduction-to-computer-vision"], [92, "introduction-to-computer-vision"]], "Introduction to neural networks": [[27, "introduction-to-neural-networks"], [92, "introduction-to-neural-networks"]], "Introduction to pandas": [[8, 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One approach": [[98, "one-vs-one-approach"]], "One Vs. One prediction": [[98, "one-vs-one-prediction"]], "One vs. Rest": [[98, "one-vs-rest"]], "One-hot encoding (OHE)": [[16, "one-hot-encoding-ohe"], [79, "one-hot-encoding-ohe"]], "One-hot encoding of the month": [[28, "one-hot-encoding-of-the-month"], [49, "one-hot-encoding-of-the-month"], [93, "one-hot-encoding-of-the-month"]], "One-hot encoding seasons": [[28, "one-hot-encoding-seasons"], [49, "one-hot-encoding-seasons"], [93, "one-hot-encoding-seasons"]], "OneHotEncoder and sparse features": [[17, "onehotencoder-and-sparse-features"], [80, "onehotencoder-and-sparse-features"]], "Online courses": [[1, "online-courses"], [9, "online-courses"]], "Operating point": [[20, "operating-point"], [40, "operating-point"], [83, "operating-point"]], "Optimization bias of hyper-parameter learning": [[19, "optimization-bias-of-hyper-parameter-learning"], [82, "optimization-bias-of-hyper-parameter-learning"]], "Optimization bias of parameter learning": [[19, 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not cover?": [[29, "other-approaches-what-did-we-not-cover"], [50, "other-approaches-what-did-we-not-cover"], [94, "other-approaches-what-did-we-not-cover"]], "Other commonly used preprocessing steps": [[48, "other-commonly-used-preprocessing-steps"], [91, "other-commonly-used-preprocessing-steps"]], "Other possible preprocessing?": [[21, "other-possible-preprocessing"], [41, "other-possible-preprocessing"], [84, "other-possible-preprocessing"]], "Other software package": [[28, "other-software-package"], [49, "other-software-package"], [93, "other-software-package"]], "Other tools": [[23, "other-tools"]], "Other tools for preprocessing": [[48, "other-tools-for-preprocessing"], [91, "other-tools-for-preprocessing"]], "Other typical NLP tasks": [[48, "other-typical-nlp-tasks"], [91, "other-typical-nlp-tasks"]], "Other useful arguments of KNeighborsClassifier": [[15, "other-useful-arguments-of-kneighborsclassifier"], [78, "other-useful-arguments-of-kneighborsclassifier"]], "Other ways to 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"prevalence-of-ml"], [75, "prevalence-of-ml"]], "Principles of effective communication": [[30, "principles-of-effective-communication"], [51, "principles-of-effective-communication"], [95, "principles-of-effective-communication"]], "Principles of good explanations (~15 min)": [[30, "principles-of-good-explanations-15-min"], [51, "principles-of-good-explanations-15-min"], [95, "principles-of-good-explanations-15-min"]], "Problem formulation": [[47, "problem-formulation"], [90, "problem-formulation"]], "Problem: Different transformations on different columns": [[16, "problem-different-transformations-on-different-columns"], [79, "problem-different-transformations-on-different-columns"]], "Problems with exhaustive grid search": [[19, "problems-with-exhaustive-grid-search"], [38, "problems-with-exhaustive-grid-search"], [39, "problems-with-exhaustive-grid-search"], [82, "problems-with-exhaustive-grid-search"]], "Problems with single train/validation split": [[14, "problems-with-single-train-validation-split"], [77, "problems-with-single-train-validation-split"]], "Pros of k-NNs for supervised learning": [[15, "pros-of-k-nns-for-supervised-learning"], [78, "pros-of-k-nns-for-supervised-learning"]], "Pros, cons, parameters and hyperparameters of different ML models": [[99, "pros-cons-parameters-and-hyperparameters-of-different-ml-models"]], "Python and Conda": [[10, "python-and-conda"]], "Python requirements/resources": [[12, "python-requirements-resources"]], "Python resources": [[9, "python-resources"]], "Question": [[15, "question"], [78, "question"]], "Question for you": [[26, "question-for-you"], [89, "question-for-you"]], "Question for you to ponder on": [[36, "question-for-you-to-ponder-on"], [37, "question-for-you-to-ponder-on"]], "Questions for class discussion": [[47, "questions-for-class-discussion"], [90, "questions-for-class-discussion"]], "Questions for class discussion (hyperparameter optimization)": [[19, 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"recommender-systems"]], "Recommender systems intro and motivation": [[47, "recommender-systems-intro-and-motivation"], [90, "recommender-systems-intro-and-motivation"]], "Recommender systems problem": [[47, "recommender-systems-problem"], [90, "recommender-systems-problem"]], "Recursive feature elimination (RFE)": [[24, "recursive-feature-elimination-rfe"], [44, "recursive-feature-elimination-rfe"], [87, "recursive-feature-elimination-rfe"]], "Reference Material": [[1, "reference-material"]], "Reference material": [[9, null]], "References": [[29, "references"], [50, "references"], [94, "references"]], "Registration": [[101, "registration"]], "Registration, waitlist and prerequisites": [[12, "registration-waitlist-and-prerequisites"]], "Regression scoring functions": [[21, "regression-scoring-functions"], [41, "regression-scoring-functions"], [84, "regression-scoring-functions"]], "Regression with k-nearest neighbours (k-NNs)": [[15, "regression-with-k-nearest-neighbours-k-nns"], [78, "regression-with-k-nearest-neighbours-k-nns"]], "Relation of C and the fundamental trade-off": [[15, "relation-of-c-and-the-fundamental-trade-off"], [78, "relation-of-c-and-the-fundamental-trade-off"]], "Relation of gamma and the fundamental trade-off": [[15, "relation-of-gamma-and-the-fundamental-trade-off"], [78, "relation-of-gamma-and-the-fundamental-trade-off"]], "Relevant companion materials": [[9, "relevant-companion-materials"]], "Relevant papers": [[22, "relevant-papers"], [42, "relevant-papers"], [85, "relevant-papers"]], "Relevant papers and resources": [[20, "relevant-papers-and-resources"], [83, "relevant-papers-and-resources"]], "Relevant resources": [[24, "relevant-resources"], [87, "relevant-resources"]], "Reminder": [[47, "reminder"], [90, "reminder"]], "Renaming columns with df.rename()": [[8, "renaming-columns-with-df-rename"]], "Render set-up (I already did these):": [[96, "render-set-up-i-already-did-these"]], "Report format": [[7, "report-format"]], "Requirements (I already did these)": [[96, "requirements-i-already-did-these"]], "Resources": [[25, "resources"], [26, "resources"], [47, "resources"], [88, "resources"], [89, "resources"], [90, "resources"]], "Reuse your running examples": [[30, "reuse-your-running-examples"], [51, "reuse-your-running-examples"], [95, "reuse-your-running-examples"]], "Ridge": [[18, "ridge"], [81, "ridge"]], "Ridge on the California housing dataset": [[18, "ridge-on-the-california-housing-dataset"], [81, "ridge-on-the-california-housing-dataset"]], "RidgeCV": [[21, "ridgecv"], [41, "ridgecv"], [84, "ridgecv"]], "Root mean squared error or RMSE": [[21, "root-mean-squared-error-or-rmse"], [41, "root-mean-squared-error-or-rmse"], [84, "root-mean-squared-error-or-rmse"]], "SHAP (SHapley Additive exPlanations)": [[23, "shap-shapley-additive-explanations"]], "SHAP (SHapley Additive exPlanations) introduction": [[23, "shap-shapley-additive-explanations-introduction"], [43, "shap-shapley-additive-explanations-introduction"], [86, "shap-shapley-additive-explanations-introduction"]], "SHAP Dependence plot": [[23, "shap-dependence-plot"]], "SHAP Force plot": [[23, "shap-force-plot"]], "SHAP on LGBM model": [[23, "shap-on-lgbm-model"]], "SHAP plots": [[43, "shap-plots"], [86, "shap-plots"]], "SHAP summary plot": [[23, "shap-summary-plot"]], "SHAP waterfall plot": [[23, "shap-waterfall-plot"]], "SHAP: visualizing multiple predictions": [[23, "shap-visualizing-multiple-predictions"]], "SMOTE idea": [[20, "smote-idea"], [83, "smote-idea"]], "SMOTE: Synthetic Minority Over-sampling Technique": [[20, "smote-synthetic-minority-over-sampling-technique"], [83, "smote-synthetic-minority-over-sampling-technique"]], "SVM Regressor": [[15, "svm-regressor"], [78, "svm-regressor"]], "Saving the model": [[96, "saving-the-model"]], "Saving time and scaling products": [[12, "saving-time-and-scaling-products"], [75, "saving-time-and-scaling-products"]], "Scaling": [[16, "scaling"], [79, "scaling"]], "Scaling using scikit-learn\u2019s StandardScaler": [[16, "scaling-using-scikit-learn-s-standardscaler"], [79, "scaling-using-scikit-learn-s-standardscaler"]], "Search over multiple hyperparameters": [[15, "search-over-multiple-hyperparameters"], [78, "search-over-multiple-hyperparameters"]], "Seasonality and trends": [[28, "seasonality-and-trends"], [49, "seasonality-and-trends"], [93, "seasonality-and-trends"]], "Section 101": [[31, null]], "Section 102": [[32, null]], "Section 103": [[63, null]], "Select all of the following statements which are True (iClicker)": [[12, "select-all-of-the-following-statements-which-are-true-iclicker"], [75, "select-all-of-the-following-statements-which-are-true-iclicker"]], "Sending a request to the API": [[96, "sending-a-request-to-the-api"]], "Setting up": [[5, "setting-up"]], "Setting up a virtual environment: Conda environments": [[10, "setting-up-a-virtual-environment-conda-environments"]], "Setting up coding environment": [[10, null]], "Setting up your computer for the course": [[12, "setting-up-your-computer-for-the-course"]], "Short posts/articles": [[9, "short-posts-articles"]], "Sigmoid vs. Softmax": [[27, "sigmoid-vs-softmax"], [92, "sigmoid-vs-softmax"]], "Sign of the coefficients": [[18, "sign-of-the-coefficients"], [81, "sign-of-the-coefficients"]], "Silhouette distance for a sample": [[25, "silhouette-distance-for-a-sample"], [88, "silhouette-distance-for-a-sample"]], "Similarity between examples": [[15, "similarity-between-examples"], [78, "similarity-between-examples"]], "Simple feature engineering for our problem.": [[97, "simple-feature-engineering-for-our-problem"]], "Simple train/test split": [[14, "simple-train-test-split"], [77, "simple-train-test-split"]], "SimpleFeature correlations": [[23, "simplefeature-correlations"], [86, "simplefeature-correlations"]], "Single validation set": [[33, "single-validation-set"], [64, "single-validation-set"]], "Slowest method: nested loop": [[8, "slowest-method-nested-loop"]], "Software": [[0, "software"]], "Some important hyperparameters:": [[22, "some-important-hyperparameters"], [42, "some-important-hyperparameters"], [85, "some-important-hyperparameters"]], "Some key takeaways": [[96, "some-key-takeaways"]], "Some quotes on feature engineering": [[24, "some-quotes-on-feature-engineering"], [87, "some-quotes-on-feature-engineering"]], "Some terminology related to trees": [[13, "some-terminology-related-to-trees"], [76, "some-terminology-related-to-trees"]], "Some ways to pick hyperparameters:": [[19, "some-ways-to-pick-hyperparameters"], [82, "some-ways-to-pick-hyperparameters"]], "Sorting a dataframe with df.sort_values()": [[8, "sorting-a-dataframe-with-df-sort-values"]], "Spam/non spam toy example": [[17, "spam-non-spam-toy-example"], [80, "spam-non-spam-toy-example"]], "Specific questions": [[4, "specific-questions"]], "Stacking": [[22, "stacking"], [42, "stacking"], [85, "stacking"]], "Steps to train a classifier using sklearn": [[13, "steps-to-train-a-classifier-using-sklearn"], [76, "steps-to-train-a-classifier-using-sklearn"]], "Stratified Splits": [[20, "stratified-splits"], [83, "stratified-splits"]], "Strengths and weaknesses": [[22, "strengths-and-weaknesses"], [42, "strengths-and-weaknesses"], [85, "strengths-and-weaknesses"]], "Strengths of linear models": [[18, "strengths-of-linear-models"], [81, "strengths-of-linear-models"]], "Study tips": [[99, "study-tips"]], "Submitting on Gradescope": [[7, "submitting-on-gradescope"]], "Summary": [[12, "summary"], [15, "summary"], [22, "summary"], [27, "summary"], [29, "summary"], [42, "summary"], [48, "summary"], [50, "summary"], [75, "summary"], [78, "summary"], [85, "summary"], [91, "summary"], [92, "summary"], [94, "summary"]], "Summary and reflection": [[14, "summary-and-reflection"], [77, "summary-and-reflection"]], "Summary of linear models": [[18, "summary-of-linear-models"], [81, "summary-of-linear-models"]], "Summary of train, validation, test, and deployment data": [[14, "summary-of-train-validation-test-and-deployment-data"], [77, "summary-of-train-validation-test-and-deployment-data"]], "Summary: Pros and cons": [[26, "summary-pros-and-cons"], [89, "summary-pros-and-cons"]], "Supervised approach to rating prediction": [[47, "supervised-approach-to-rating-prediction"], [90, "supervised-approach-to-rating-prediction"]], "Supervised learning": [[25, "supervised-learning"], [88, "supervised-learning"]], "Supervised learning (Reminder)": [[13, "supervised-learning-reminder"], [76, "supervised-learning-reminder"]], "Supervised learning vs. Unsupervised learning": [[13, "supervised-learning-vs-unsupervised-learning"], [76, "supervised-learning-vs-unsupervised-learning"]], "Supervised machine learning": [[12, "supervised-machine-learning"], [75, "supervised-machine-learning"]], "Support Vector Machines (SVMs) with RBF kernel [video]": [[15, "support-vector-machines-svms-with-rbf-kernel-video"], [78, "support-vector-machines-svms-with-rbf-kernel-video"]], "Support vectors": [[15, "support-vectors"], [78, "support-vectors"]], "Survival analysis": [[99, "survival-analysis"]], "Survival plots": [[29, "survival-plots"], [50, "survival-plots"], [94, "survival-plots"]], "Syllabus": [[1, "syllabus"], [101, null]], "TAs": [[1, "tas"], [101, "tas"]], "Tabular data": [[13, "tabular-data"], [76, "tabular-data"]], "Take-home message": [[26, "take-home-message"], [89, "take-home-message"]], "Teaching Team": [[101, "teaching-team"]], "Terminology": [[27, "terminology"], [92, "terminology"]], "Terminology [video]": [[13, "terminology-video"], [76, "terminology-video"]], "Testing your git installation": [[5, "testing-your-git-installation"]], "The Dataset": [[64, "the-dataset"]], "The Netflix prize": [[22, "the-netflix-prize"], [85, "the-netflix-prize"]], "The __ syntax": [[19, "the-syntax"], [38, "the-syntax"], [39, "the-syntax"], [82, "the-syntax"]], "The best features may be dependent on the model you use.": [[24, "the-best-features-may-be-dependent-on-the-model-you-use"], [87, "the-best-features-may-be-dependent-on-the-model-you-use"]], "The golden rule ": [[14, "the-golden-rule"], [77, "the-golden-rule"]], "The random forests classifier": [[22, "the-random-forests-classifier"], [42, "the-random-forests-classifier"], [85, "the-random-forests-classifier"]], "The sigmoid function": [[18, "the-sigmoid-function"], [81, "the-sigmoid-function"]], "The teaching team": [[1, "the-teaching-team"]], "The \u201cfundamental tradeoff\u201d of supervised learning:": [[14, "the-fundamental-tradeoff-of-supervised-learning"], [77, "the-fundamental-tradeoff-of-supervised-learning"]], "The \u201cperfect\u201d spaghetti sauce": [[25, "the-perfect-spaghetti-sauce"], [88, "the-perfect-spaghetti-sauce"]], "Things to watch out for": [[30, "things-to-watch-out-for"], [51, "things-to-watch-out-for"], [95, "things-to-watch-out-for"]], "Thresholding": [[40, "thresholding"]], "Time series": [[99, "time-series"]], "Time to event and censoring": [[29, "time-to-event-and-censoring"], [50, "time-to-event-and-censoring"], [94, "time-to-event-and-censoring"]], "Tokenization": [[48, "tokenization"], [91, "tokenization"]], "Topic modeling": [[48, "topic-modeling"], [91, "topic-modeling"]], "Topic modeling motivation": [[48, "topic-modeling-motivation"], [91, "topic-modeling-motivation"]], "Topic modeling pipeline": [[48, "topic-modeling-pipeline"], [91, "topic-modeling-pipeline"]], "Topic modeling toy example": [[48, "topic-modeling-toy-example"], [91, "topic-modeling-toy-example"]], "Toy datasets": [[13, "toy-datasets"], [76, "toy-datasets"]], "Traditional time series approaches": [[28, "traditional-time-series-approaches"], [49, "traditional-time-series-approaches"], [93, "traditional-time-series-approaches"]], "Train/test split for temporal data": [[28, "train-test-split-for-temporal-data"], [49, "train-test-split-for-temporal-data"], [93, "train-test-split-for-temporal-data"]], "Train/test splits": [[28, "train-test-splits"], [49, "train-test-splits"], [93, "train-test-splits"]], "Train/validation/test split": [[14, "train-validation-test-split"], [77, "train-validation-test-split"]], "Training a supervised machine learning model with X and y": [[12, "training-a-supervised-machine-learning-model-with-x-and-y"], [75, "training-a-supervised-machine-learning-model-with-x-and-y"]], "Training data for the motivating example": [[18, "training-data-for-the-motivating-example"], [81, "training-data-for-the-motivating-example"]], "Training error vs. Generalization error": [[14, "training-error-vs-generalization-error"], [77, "training-error-vs-generalization-error"]], "Training models with transformed data": [[17, "training-models-with-transformed-data"], [80, "training-models-with-transformed-data"]], "Training on the full corpus": [[96, "training-on-the-full-corpus"]], "Training random forests and gradient boosted trees": [[30, "training-random-forests-and-gradient-boosted-trees"], [51, "training-random-forests-and-gradient-boosted-trees"], [95, "training-random-forests-and-gradient-boosted-trees"]], "Transfer learning": [[27, "transfer-learning"], [92, "transfer-learning"]], "Transformations on the toy data": [[17, "transformations-on-the-toy-data"], [80, "transformations-on-the-toy-data"]], "Transforming the targets": [[21, "transforming-the-targets"], [41, "transforming-the-targets"], [84, "transforming-the-targets"]], "Transparency and explainability of ML models: Motivation": [[23, "transparency-and-explainability-of-ml-models-motivation"], [43, "transparency-and-explainability-of-ml-models-motivation"], [86, "transparency-and-explainability-of-ml-models-motivation"]], "Tree-based ensemble models": [[22, "tree-based-ensemble-models"], [85, "tree-based-ensemble-models"]], "Tree-based models": [[22, "tree-based-models"], [85, "tree-based-models"]], "Try out this moment predictor": [[96, "try-out-this-moment-predictor"]], "Tuning alpha hyperparameter of Ridge": [[21, "tuning-alpha-hyperparameter-of-ridge"], [41, "tuning-alpha-hyperparameter-of-ridge"], [84, "tuning-alpha-hyperparameter-of-ridge"]], "Types of censoring": [[29, "types-of-censoring"], [50, "types-of-censoring"], [94, "types-of-censoring"]], "Types of errors": [[14, "types-of-errors"], [77, "types-of-errors"]], "Types of machine learning": [[12, "types-of-machine-learning"], [25, "types-of-machine-learning"], [75, "types-of-machine-learning"], [88, "types-of-machine-learning"]], "Types of problems involving time series": [[28, "types-of-problems-involving-time-series"], [49, "types-of-problems-involving-time-series"], [93, "types-of-problems-involving-time-series"]], "Types of questions we might want to answer:": [[29, "types-of-questions-we-might-want-to-answer"], [50, "types-of-questions-we-might-want-to-answer"], [94, "types-of-questions-we-might-want-to-answer"]], "Typical steps to build a supervised machine learning model": [[33, "typical-steps-to-build-a-supervised-machine-learning-model"], [64, "typical-steps-to-build-a-supervised-machine-learning-model"]], "UBC CPSC 330: Applied Machine Learning (2024W1)": [[1, null]], "Ubuntu Users": [[5, "ubuntu-users"]], "Underfitting": [[14, "underfitting"], [77, "underfitting"]], "Underfitting, overfitting, the fundamental trade-off, the golden rule [video]": [[14, "underfitting-overfitting-the-fundamental-trade-off-the-golden-rule-video"], [77, "underfitting-overfitting-the-fundamental-trade-off-the-golden-rule-video"]], "Undersampling": [[20, "undersampling"], [83, "undersampling"]], "Understanding the problem": [[96, "understanding-the-problem"]], "Unequally spaced time points": [[28, "unequally-spaced-time-points"], [49, "unequally-spaced-time-points"], [93, "unequally-spaced-time-points"]], "Unsupervised learning": [[25, "unsupervised-learning"], [88, "unsupervised-learning"]], "Updates to assignments": [[7, "updates-to-assignments"]], "Use of AI in the course": [[101, "use-of-ai-in-the-course"]], "Use our template to create a repository": [[7, "use-our-template-to-create-a-repository"]], "Using OVR and OVO as wrappers": [[98, "using-ovr-and-ovo-as-wrappers"]], "Using SMOTE": [[20, "using-smote"], [83, "using-smote"]], "Using Silhouette scores to select the number of clusters": [[25, "using-silhouette-scores-to-select-the-number-of-clusters"], [88, "using-silhouette-scores-to-select-the-number-of-clusters"]], "Using multiple metrics in GridSearchCV or RandomizedSearchCV": [[21, "using-multiple-metrics-in-gridsearchcv-or-randomizedsearchcv"], [41, "using-multiple-metrics-in-gridsearchcv-or-randomizedsearchcv"], [84, "using-multiple-metrics-in-gridsearchcv-or-randomizedsearchcv"]], "Using pre-trained models as feature extractor": [[27, "using-pre-trained-models-as-feature-extractor"], [92, "using-pre-trained-models-as-feature-extractor"]], "Using pre-trained models out-of-the-box": [[27, "using-pre-trained-models-out-of-the-box"], [92, "using-pre-trained-models-out-of-the-box"]], "Using regression metrics with scikit-learn": [[21, "using-regression-metrics-with-scikit-learn"], [41, "using-regression-metrics-with-scikit-learn"], [84, "using-regression-metrics-with-scikit-learn"]], "Viewing the transformed data as a dataframe": [[17, "viewing-the-transformed-data-as-a-dataframe"], [80, "viewing-the-transformed-data-as-a-dataframe"]], "Virtual environment": [[10, "virtual-environment"]], "Visualization": [[9, "visualization"]], "Visualizing the parameter grid as a heatmap": [[19, "visualizing-the-parameter-grid-as-a-heatmap"], [38, "visualizing-the-parameter-grid-as-a-heatmap"], [39, "visualizing-the-parameter-grid-as-a-heatmap"], [82, "visualizing-the-parameter-grid-as-a-heatmap"]], "Visualizing your results": [[30, "visualizing-your-results"], [51, "visualizing-your-results"], [95, "visualizing-your-results"]], "Warning": [[13, null], [76, null]], "Warnings about feature selection": [[24, "warnings-about-feature-selection"], [24, "id8"], [44, "warnings-about-feature-selection"], [87, "warnings-about-feature-selection"], [87, "id8"]], "Weaknesses": [[22, "weaknesses"], [42, "weaknesses"], [85, "weaknesses"]], "Web app on a real server": [[96, "web-app-on-a-real-server"]], "Web app on local server": [[96, "web-app-on-local-server"]], "What all transformations we need to apply on the dataset?": [[79, "what-all-transformations-we-need-to-apply-on-the-dataset"]], "What and Why": [[10, "what-and-why"]], "What are git and GitHub?": [[5, null]], "What are the options?": [[16, "what-are-the-options"], [79, "what-are-the-options"]], "What are we exactly learning?": [[18, "what-are-we-exactly-learning"], [81, "what-are-we-exactly-learning"]], "What did we cover?": [[47, "what-did-we-cover"], [90, "what-did-we-cover"], [96, "what-did-we-cover"]], "What did we learn today?": [[14, "what-did-we-learn-today"], [16, "what-did-we-learn-today"], [17, "what-did-we-learn-today"], [20, "what-did-we-learn-today"], [21, "what-did-we-learn-today"], [30, "what-did-we-learn-today"], [51, "what-did-we-learn-today"], [77, "what-did-we-learn-today"], [79, "what-did-we-learn-today"], [80, "what-did-we-learn-today"], [83, "what-did-we-learn-today"], [84, "what-did-we-learn-today"], [95, "what-did-we-learn-today"]], "What does this have to do with applied ML?": [[30, "what-does-this-have-to-do-with-applied-ml"], [51, "what-does-this-have-to-do-with-applied-ml"], [95, "what-does-this-have-to-do-with-applied-ml"]], "What does this mean for us, when we\u2019re trying to make claims about our data?": [[30, "what-does-this-mean-for-us-when-we-re-trying-to-make-claims-about-our-data"], [51, "what-does-this-mean-for-us-when-we-re-trying-to-make-claims-about-our-data"], [95, "what-does-this-mean-for-us-when-we-re-trying-to-make-claims-about-our-data"]], "What if we apply OHE?": [[17, "what-if-we-apply-ohe"], [80, "what-if-we-apply-ohe"]], "What is Natural Language Processing (NLP)?": [[48, "what-is-natural-language-processing-nlp"], [91, "what-is-natural-language-processing-nlp"]], "What is a recommender system?": [[47, "what-is-a-recommender-system"], [90, "what-is-a-recommender-system"]], "What is clustering?": [[25, "what-is-clustering"], [88, "what-is-clustering"]], "What is deployment?": [[96, "what-is-deployment"]], "What is feature engineering?": [[24, "what-is-feature-engineering"], [87, "what-is-feature-engineering"]], "What is feature selection?": [[24, "what-is-feature-selection"], [44, "what-is-feature-selection"], [87, "what-is-feature-selection"]], "What is grid search?": [[30, "what-is-grid-search"], [51, "what-is-grid-search"], [95, "what-is-grid-search"]], "What is model interpretability?": [[23, "what-is-model-interpretability"], [43, "what-is-model-interpretability"], [86, "what-is-model-interpretability"]], "What is supervised machine learning (ML)?": [[12, "what-is-supervised-machine-learning-ml"], [75, "what-is-supervised-machine-learning-ml"]], "What is \u201cpositive\u201d and \u201cnegative\u201d?": [[20, "what-is-positive-and-negative"], [40, "what-is-positive-and-negative"], [83, "what-is-positive-and-negative"]], "What kind of estimators can we combine?": [[22, "what-kind-of-estimators-can-we-combine"], [42, "what-kind-of-estimators-can-we-combine"], [85, "what-kind-of-estimators-can-we-combine"]], "What next?": [[96, "what-next"]], "What should be the loss? 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metrics", "Lecture 12: Ensembles", "Lecture 13: Feature importances and model transparency", "Lecture 14: Feature engineering and feature selection", "Lecture 15: K-Means Clustering", "Lecture 16: More Clustering", "Lecture 19: Multi-class classification and introduction to computer vision", "Lecture 20: Time series", "Lecture 21: Survival analysis", "Lecture 22: Communication", "Section 101", "Section 102", "Lecture 3: ML Fundamentals Class Demo", "Lecture 4: Class demo", "Lecture 5 and 6: Class demo", "Lectures 7: Class demo", "Lectures 7: Class demo", "Lectures 8: Class demo", "Lectures 8: Class demo", "Lecture 9: Class demo", "Lecture 10: Class demo", "Lecture 12: Ensembles", "Lecture 13: Class demo", "Lecture 14: Class demo", "Lecture 15: Clustering class demo", "Lecture 15: Class demo", "Lecture 17: Class demo", "Lecture 18: Class demo", "Lecture 20: Class demo", "Lecture 21: Class demo (Based on the lecture notes)", "Lecture 22: Class demo (Based on the lecture notes)", "Lecture 1", "Lecture 10", "Lecture 11", "Lecture 2", "Lecture 3", "Lecture 4", "Lecture 5", "Lecture 6", "Lecture 7", "Lecture 8", "Lecture 9", "Section 103", "Lecture 3 Demo: ML Fundamentals", "Lecture 1", "Lecture 11", "Lecture 2", "Lecture 3", "Lecture 4", "Lecture 5", "Lecture 6", "Lecture 7", "Lecture 8", "Lecture 9", "Lecture 1: Course Introduction", "Lecture 2: Terminology, Baselines, Decision Trees", "Lecture 3: Machine Learning Fundamentals", "Lecture 4: \\(k\\)-Nearest Neighbours and SVM RBFs", "Lecture 5: Preprocessing and sklearn pipelines", "Lecture 6: sklearn ColumnTransformer and Text Features", "Lecture 7: Linear Models", "Lecture 8: Hyperparameter Optimization and Optimization Bias", "Lecture 9: Classification metrics", "Lecture 10: Regression metrics", "Lecture 12: Ensembles", "Lecture 13: Feature importances and model transparency", "Lecture 14: Feature engineering and feature selection", "Lecture 15: K-Means Clustering", "Lecture 16: More Clustering", "Lecture 17: Recommender Systems", "Lecture 18: Introduction to natural language processing", "Lecture 19: Multi-class classification and introduction to computer vision", "Lecture 20: Time series", "Lecture 21: Survival analysis", "Lecture 22: Communication", "Lecture 24: Deployment and conclusion", "Appendix A: Demo of feature engineering for text data", "Appendix B: Multi-class, meta-strategies", "Final exam preparation: guiding questions", "<no title>", "Syllabus"], "titleterms": {"": [12, 14, 15, 16, 17, 20, 21, 23, 28, 30, 35, 40, 41, 43, 45, 49, 51, 64, 75, 77, 78, 79, 80, 83, 84, 86, 93, 95], "0": [22, 85], "1": [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 38, 39, 42, 47, 50, 51, 52, 65, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 99], "10": [21, 41, 53, 84], "101": 31, "102": 32, "103": 63, "11": [54, 66], "12": [22, 42, 85], "13": [23, 43, 86], "14": [24, 44, 87], "15": [25, 30, 45, 46, 51, 88, 95, 96], "16": [26, 89], "17": [47, 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