exercise-distributions.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**This notebook is an exercise in the [Data Visualization](https://www.kaggle.com/learn/data-visualization) course.  You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/distributions).**\n\n---\n","metadata":{}},{"cell_type":"markdown","source":"In this exercise, you will use your new knowledge to propose a solution to a real-world scenario.  To succeed, you will need to import data into Python, answer questions using the data, and generate **histograms** and **density plots** to understand patterns in the data.\n\n## Scenario\n\nYou'll work with a real-world dataset containing information collected from microscopic images of breast cancer tumors, similar to the image below.\n\n![ex4_cancer_image](https://i.imgur.com/qUESsJe.png)\n\nEach tumor has been labeled as either [**benign**](https://en.wikipedia.org/wiki/Benign_tumor) (_noncancerous_) or **malignant** (_cancerous_).\n\nTo learn more about how this kind of data is used to create intelligent algorithms to classify tumors in medical settings, **watch the short video [at this link](https://www.youtube.com/watch?v=9Mz84cwVmS0)**.","metadata":{}},{"cell_type":"markdown","source":"\n\n## Setup\n\nRun the next cell to import and configure the Python libraries that you need to complete the exercise.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\npd.plotting.register_matplotlib_converters()\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport seaborn as sns\nprint(\"Setup Complete\")","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:49:21.495364Z","iopub.execute_input":"2022-10-31T08:49:21.495693Z","iopub.status.idle":"2022-10-31T08:49:21.502698Z","shell.execute_reply.started":"2022-10-31T08:49:21.495666Z","shell.execute_reply":"2022-10-31T08:49:21.502075Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Setup Complete\n","output_type":"stream"}]},{"cell_type":"markdown","source":"The questions below will give you feedback on your work. Run the following cell to set up our feedback system.","metadata":{}},{"cell_type":"code","source":"# Set up code checking\n!pip install -U -t /kaggle/working/ git+https://github.com/Kaggle/learntools.git@master\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.data_viz_to_coder.ex5 import *\nprint(\"Setup Complete\")","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:49:24.180222Z","iopub.execute_input":"2022-10-31T08:49:24.180544Z","iopub.status.idle":"2022-10-31T08:49:38.846762Z","shell.execute_reply.started":"2022-10-31T08:49:24.180520Z","shell.execute_reply":"2022-10-31T08:49:38.845625Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Collecting git+https://github.com/Kaggle/learntools.git@master\n  Cloning https://github.com/Kaggle/learntools.git (to revision master) to /tmp/pip-req-build-4ekfurwo\n  Running command git clone --filter=blob:none --quiet https://github.com/Kaggle/learntools.git /tmp/pip-req-build-4ekfurwo\n  Resolved https://github.com/Kaggle/learntools.git to commit bcb7329ecdcd9c40e80637aba228edd4cb4950b4\n  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hBuilding wheels for collected packages: learntools\n  Building wheel for learntools (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for learntools: filename=learntools-0.3.4-py3-none-any.whl size=267787 sha256=5627bb143e4b5504227155400e4683117723a49a11b7bfc78efff84816d184a6\n  Stored in directory: /tmp/pip-ephem-wheel-cache-o3wnddeq/wheels/96/98/04/32b386c4641f1aea964eebb0148dc4632b25195ed8e54869c8\nSuccessfully built learntools\nInstalling collected packages: learntools\nSuccessfully installed learntools-0.3.4\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0mSetup Complete\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Step 1: Load the data\n\nIn this step, you will load the data.\n- Load the data file into a DataFrame called `cancer_data`.  \n- The corresponding filepath is `cancer_filepath`.  \n- Use the `\"Id\"` column to label the rows.","metadata":{}},{"cell_type":"code","source":"# Path of the files to read\ncancer_filepath = \"../input/cancer.csv\"\n\n# Fill in the line below to read the file into a variable cancer_data\ncancer_data = pd.read_csv(cancer_filepath, index_col=\"Id\")\n\n# Run the line below with no changes to check that you've loaded the data correctly\nstep_1.check()","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:49:38.849547Z","iopub.execute_input":"2022-10-31T08:49:38.849948Z","iopub.status.idle":"2022-10-31T08:49:38.872472Z","shell.execute_reply.started":"2022-10-31T08:49:38.849913Z","shell.execute_reply":"2022-10-31T08:49:38.871120Z"},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.5, \"interactionType\": 1, \"questionType\": 1, \"questionId\": \"1_LoadCancerData\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}}]},{"cell_type":"code","source":"# Lines below will give you a hint or solution code\n#step_1.hint()\nstep_1.solution()","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:49:43.610799Z","iopub.execute_input":"2022-10-31T08:49:43.611128Z","iopub.status.idle":"2022-10-31T08:49:43.619981Z","shell.execute_reply.started":"2022-10-31T08:49:43.611104Z","shell.execute_reply":"2022-10-31T08:49:43.618663Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 1, \"questionId\": \"1_LoadCancerData\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: \n```python\n\ncancer_data = pd.read_csv(cancer_filepath, index_col=\"Id\")\n\n```","text/markdown":"<span style=\"color:#33cc99\">Solution:</span> \n```python\n\ncancer_data = pd.read_csv(cancer_filepath, index_col=\"Id\")\n\n```"},"metadata":{}}]},{"cell_type":"markdown","source":"## Step 2: Review the data\n\nUse a Python command to print the first 5 rows of the data.","metadata":{}},{"cell_type":"code","source":"# Print the first five rows of the data\ncancer_data.head() # Your code here","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:50:06.343922Z","iopub.execute_input":"2022-10-31T08:50:06.344290Z","iopub.status.idle":"2022-10-31T08:50:06.388181Z","shell.execute_reply.started":"2022-10-31T08:50:06.344239Z","shell.execute_reply":"2022-10-31T08:50:06.387223Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"         Diagnosis  Radius (mean)  Texture (mean)  Perimeter (mean)  \\\nId                                                                    \n8510426          B         13.540           14.36             87.46   \n8510653          B         13.080           15.71             85.63   \n8510824          B          9.504           12.44             60.34   \n854941           B         13.030           18.42             82.61   \n85713702         B          8.196           16.84             51.71   \n\n          Area (mean)  Smoothness (mean)  Compactness (mean)  \\\nId                                                             \n8510426         566.3            0.09779             0.08129   \n8510653         520.0            0.10750             0.12700   \n8510824         273.9            0.10240             0.06492   \n854941          523.8            0.08983             0.03766   \n85713702        201.9            0.08600             0.05943   \n\n          Concavity (mean)  Concave points (mean)  Symmetry (mean)  ...  \\\nId                                                                  ...   \n8510426            0.06664               0.047810           0.1885  ...   \n8510653            0.04568               0.031100           0.1967  ...   \n8510824            0.02956               0.020760           0.1815  ...   \n854941             0.02562               0.029230           0.1467  ...   \n85713702           0.01588               0.005917           0.1769  ...   \n\n          Radius (worst)  Texture (worst)  Perimeter (worst)  Area (worst)  \\\nId                                                                           \n8510426           15.110            19.26              99.70         711.2   \n8510653           14.500            20.49              96.09         630.5   \n8510824           10.230            15.66              65.13         314.9   \n854941            13.300            22.81              84.46         545.9   \n85713702           8.964            21.96              57.26         242.2   \n\n          Smoothness (worst)  Compactness (worst)  Concavity (worst)  \\\nId                                                                     \n8510426              0.14400              0.17730            0.23900   \n8510653              0.13120              0.27760            0.18900   \n8510824              0.13240              0.11480            0.08867   \n854941               0.09701              0.04619            0.04833   \n85713702             0.12970              0.13570            0.06880   \n\n          Concave points (worst)  Symmetry (worst)  Fractal dimension (worst)  \nId                                                                             \n8510426                  0.12880            0.2977                    0.07259  \n8510653                  0.07283            0.3184                    0.08183  \n8510824                  0.06227            0.2450                    0.07773  \n854941                   0.05013            0.1987                    0.06169  \n85713702                 0.02564            0.3105                    0.07409  \n\n[5 rows x 31 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Diagnosis</th>\n      <th>Radius (mean)</th>\n      <th>Texture (mean)</th>\n      <th>Perimeter (mean)</th>\n      <th>Area (mean)</th>\n      <th>Smoothness (mean)</th>\n      <th>Compactness (mean)</th>\n      <th>Concavity (mean)</th>\n      <th>Concave points (mean)</th>\n      <th>Symmetry (mean)</th>\n      <th>...</th>\n      <th>Radius (worst)</th>\n      <th>Texture (worst)</th>\n      <th>Perimeter (worst)</th>\n      <th>Area (worst)</th>\n      <th>Smoothness (worst)</th>\n      <th>Compactness (worst)</th>\n      <th>Concavity (worst)</th>\n      <th>Concave points (worst)</th>\n      <th>Symmetry (worst)</th>\n      <th>Fractal dimension (worst)</th>\n    </tr>\n    <tr>\n      <th>Id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8510426</th>\n      <td>B</td>\n      <td>13.540</td>\n      <td>14.36</td>\n      <td>87.46</td>\n      <td>566.3</td>\n      <td>0.09779</td>\n      <td>0.08129</td>\n      <td>0.06664</td>\n      <td>0.047810</td>\n      <td>0.1885</td>\n      <td>...</td>\n      <td>15.110</td>\n      <td>19.26</td>\n      <td>99.70</td>\n      <td>711.2</td>\n      <td>0.14400</td>\n      <td>0.17730</td>\n      <td>0.23900</td>\n      <td>0.12880</td>\n      <td>0.2977</td>\n      <td>0.07259</td>\n    </tr>\n    <tr>\n      <th>8510653</th>\n      <td>B</td>\n      <td>13.080</td>\n      <td>15.71</td>\n      <td>85.63</td>\n      <td>520.0</td>\n      <td>0.10750</td>\n      <td>0.12700</td>\n      <td>0.04568</td>\n      <td>0.031100</td>\n      <td>0.1967</td>\n      <td>...</td>\n      <td>14.500</td>\n      <td>20.49</td>\n      <td>96.09</td>\n      <td>630.5</td>\n      <td>0.13120</td>\n      <td>0.27760</td>\n      <td>0.18900</td>\n      <td>0.07283</td>\n      <td>0.3184</td>\n      <td>0.08183</td>\n    </tr>\n    <tr>\n      <th>8510824</th>\n      <td>B</td>\n      <td>9.504</td>\n      <td>12.44</td>\n      <td>60.34</td>\n      <td>273.9</td>\n      <td>0.10240</td>\n      <td>0.06492</td>\n      <td>0.02956</td>\n      <td>0.020760</td>\n      <td>0.1815</td>\n      <td>...</td>\n      <td>10.230</td>\n      <td>15.66</td>\n      <td>65.13</td>\n      <td>314.9</td>\n      <td>0.13240</td>\n      <td>0.11480</td>\n      <td>0.08867</td>\n      <td>0.06227</td>\n      <td>0.2450</td>\n      <td>0.07773</td>\n    </tr>\n    <tr>\n      <th>854941</th>\n      <td>B</td>\n      <td>13.030</td>\n      <td>18.42</td>\n      <td>82.61</td>\n      <td>523.8</td>\n      <td>0.08983</td>\n      <td>0.03766</td>\n      <td>0.02562</td>\n      <td>0.029230</td>\n      <td>0.1467</td>\n      <td>...</td>\n      <td>13.300</td>\n      <td>22.81</td>\n      <td>84.46</td>\n      <td>545.9</td>\n      <td>0.09701</td>\n      <td>0.04619</td>\n      <td>0.04833</td>\n      <td>0.05013</td>\n      <td>0.1987</td>\n      <td>0.06169</td>\n    </tr>\n    <tr>\n      <th>85713702</th>\n      <td>B</td>\n      <td>8.196</td>\n      <td>16.84</td>\n      <td>51.71</td>\n      <td>201.9</td>\n      <td>0.08600</td>\n      <td>0.05943</td>\n      <td>0.01588</td>\n      <td>0.005917</td>\n      <td>0.1769</td>\n      <td>...</td>\n      <td>8.964</td>\n      <td>21.96</td>\n      <td>57.26</td>\n      <td>242.2</td>\n      <td>0.12970</td>\n      <td>0.13570</td>\n      <td>0.06880</td>\n      <td>0.02564</td>\n      <td>0.3105</td>\n      <td>0.07409</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 31 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"In the dataset, each row corresponds to a different image.  The dataset has 31 different columns, corresponding to:\n- 1 column (`'Diagnosis'`) that classifies tumors as either benign (which appears in the dataset as **`B`**) or malignant (__`M`__), and\n- 30 columns containing different measurements collected from the images.\n\nUse the first 5 rows of the data to answer the questions below.","metadata":{}},{"cell_type":"code","source":"# Fill in the line below: In the first five rows of the data, what is the\n# largest value for 'Perimeter (mean)'?\nmax_perim = 87.46\n\n# Fill in the line below: What is the value for 'Radius (mean)' for the tumor with Id 8510824?\nmean_radius = 9.504\n\n# Check your answers\nstep_2.check()","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:50:37.171966Z","iopub.execute_input":"2022-10-31T08:50:37.172325Z","iopub.status.idle":"2022-10-31T08:50:37.180097Z","shell.execute_reply.started":"2022-10-31T08:50:37.172300Z","shell.execute_reply":"2022-10-31T08:50:37.178460Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.5, \"interactionType\": 1, \"questionType\": 1, \"questionId\": \"2_ReviewData\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}}]},{"cell_type":"code","source":"# Lines below will give you a hint or solution code\n#step_2.hint()\nstep_2.solution()","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:50:41.325537Z","iopub.execute_input":"2022-10-31T08:50:41.325870Z","iopub.status.idle":"2022-10-31T08:50:41.336359Z","shell.execute_reply.started":"2022-10-31T08:50:41.325846Z","shell.execute_reply":"2022-10-31T08:50:41.334244Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 1, \"questionId\": \"2_ReviewData\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: \n```python\n# Print the first five rows of the data\ncancer_data.head()\n# In the first five rows of the data, what is the\n# largest value for 'Perimeter (mean)'?\nmax_perim = 87.46\n# What is the value for 'Radius (mean)' for the tumor with Id 8510824?\nmean_radius = 9.504\n\n```","text/markdown":"<span style=\"color:#33cc99\">Solution:</span> \n```python\n# Print the first five rows of the data\ncancer_data.head()\n# In the first five rows of the data, what is the\n# largest value for 'Perimeter (mean)'?\nmax_perim = 87.46\n# What is the value for 'Radius (mean)' for the tumor with Id 8510824?\nmean_radius = 9.504\n\n```"},"metadata":{}}]},{"cell_type":"markdown","source":"## Step 3: Investigating differences","metadata":{}},{"cell_type":"markdown","source":"#### Part A\n\nUse the code cell below to create two histograms that show the distribution in values for `'Area (mean)'`, separately for both benign and malignant tumors.  (_To permit easy comparison, create a single figure containing both histograms in the code cell below._)","metadata":{}},{"cell_type":"code","source":"# Histograms for benign and maligant tumors\nsns.histplot(data=cancer_data, x='Area (mean)', hue='Diagnosis')\n\n# Check your answer\nstep_3.a.check()","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:51:06.218037Z","iopub.execute_input":"2022-10-31T08:51:06.218364Z","iopub.status.idle":"2022-10-31T08:51:06.555422Z","shell.execute_reply.started":"2022-10-31T08:51:06.218341Z","shell.execute_reply":"2022-10-31T08:51:06.554813Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.5, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3.1_PlotHist\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Lines below will give you a hint or solution code\n#step_3.a.hint()\nstep_3.a.solution_plot()","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:51:32.914274Z","iopub.execute_input":"2022-10-31T08:51:32.914595Z","iopub.status.idle":"2022-10-31T08:51:33.200334Z","shell.execute_reply.started":"2022-10-31T08:51:32.914571Z","shell.execute_reply":"2022-10-31T08:51:33.199546Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 2, \"questionId\": \"3.1_PlotHist\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: \n```python\n# Histograms for benign and maligant tumors\nsns.histplot(data=cancer_data, x='Area (mean)', hue='Diagnosis')\n\n```","text/markdown":"<span style=\"color:#33cc99\">Solution:</span> \n```python\n# Histograms for benign and maligant tumors\nsns.histplot(data=cancer_data, x='Area (mean)', hue='Diagnosis')\n\n```"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"#### Part B\n\nA researcher approaches you for help with identifying how the `'Area (mean)'` column can be used to understand the difference between benign and malignant tumors.  Based on the histograms above, \n- Do malignant tumors have higher or lower values for `'Area (mean)'` (relative to benign tumors), on average?\n- Which tumor type seems to have a larger range of potential values?","metadata":{}},{"cell_type":"code","source":"#step_3.b.hint()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Check your answer (Run this code cell to receive credit!)\nstep_3.b.solution()","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:51:37.481313Z","iopub.execute_input":"2022-10-31T08:51:37.481609Z","iopub.status.idle":"2022-10-31T08:51:37.491934Z","shell.execute_reply.started":"2022-10-31T08:51:37.481587Z","shell.execute_reply":"2022-10-31T08:51:37.489834Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 4, \"questionId\": \"3.2_ThinkHist\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: Malignant tumors have higher values for `'Area (mean)'`, on average. Malignant tumors have a larger range of potential values.","text/markdown":"<span style=\"color:#33cc99\">Solution:</span> Malignant tumors have higher values for `'Area (mean)'`, on average. Malignant tumors have a larger range of potential values."},"metadata":{}}]},{"cell_type":"markdown","source":"## Step 4: A very useful column\n\n#### Part A\n\nUse the code cell below to create two KDE plots that show the distribution in values for `'Radius (worst)'`, separately for both benign and malignant tumors.  (_To permit easy comparison, create a single figure containing both KDE plots in the code cell below._)","metadata":{}},{"cell_type":"code","source":"# KDE plots for benign and malignant tumors\nsns.kdeplot(data=cancer_data, x='Radius (worst)', hue='Diagnosis', shade=True)\n\n# Check your answer\nstep_4.a.check()","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:51:55.006751Z","iopub.execute_input":"2022-10-31T08:51:55.007102Z","iopub.status.idle":"2022-10-31T08:51:55.226985Z","shell.execute_reply.started":"2022-10-31T08:51:55.007076Z","shell.execute_reply":"2022-10-31T08:51:55.226050Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.5, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"4.1_PlotThreshold\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAA3sElEQVR4nO3deXxcdbn48c+TyTaTPZN0zdoN7A4UyiIIsgjKBVFQFhV+ei96lcu94ga4YV2uOy6AyqJWQCricosgld2yCG3pmjaFtqRNuiVN0qTZt+f3xzmBNJ0kkzYnsz3v12temTnLnGcOdJ757qKqGGOMMYMlRToAY4wx0ckShDHGmJAsQRhjjAnJEoQxxpiQLEEYY4wJKTnSAYyVgoICLSsri3QYxhgTU9asWXNAVQtD7YubBFFWVsbq1asjHYYxxsQUEdk51D6rYjLGGBOSJQhjjDEhWYIwxhgTUty0QRhjzLHo7u6mpqaGjo6OSIfiifT0dIqKikhJSQn7HEsQxhgD1NTUkJWVRVlZGSIS6XDGlKpSX19PTU0N5eXlYZ9nVUzGGAN0dHQQDAbjLjkAiAjBYHDUpSNLEMYY44rH5NDvaD6bJQhjjDEhWYIwAGzZ28yhju5Ih2FMVPH5fCxcuJA5c+awYMECfvSjH9HX1wfA6tWrufHGGyMan9cxWCO14c5nt3HPyh2kp/h4/MYzyc9IjXRIxkQFv9/PunXrAKitreXqq6+mubmZb3zjGyxatIhFixZFND6vY7ASRILb29TOr57fzrffP4+TSvL4wYrKSIdkTFSaMGECd999N3fccQeqynPPPcfFF18MwKuvvsppp53GCSecwOmnn87WrVsBaGtr40Mf+hCzZ8/msssuY/HixW9NCZSZmcmXv/xlFixYwKmnnsr+/fsBqKqq4t3vfjfz58/n3HPPZdeuXQD88Y9/ZO7cuSxYsICzzjoL4LAYnn/+eRYuXMjChQs54YQTOHTo0DF/ZksQCe6RNTWcNj1IfkYqF8+fzKPr99LUZlVNxoQybdo0ent7qa2tPWz78ccfz8qVK1m7di1Llizh1ltvBeCuu+4iLy+PzZs3881vfpM1a9a8dU5rayunnnoq69ev56yzzuKee+4B4L/+67+49tpr2bBhA9dcc81bVUhLlixhxYoVrF+/nuXLlx8R2w9/+EPuvPNO1q1bx8qVK/H7/cf8eS1BJLj/W7uH06YVAJAbSGXu1GxWbN4X4aiMiS1NTU1cccUVzJ07l89+9rNUVFQA8MILL3DllVcCMHfuXObPn//WOampqW/9+j/ppJOoqqoC4OWXX+bqq68G4KMf/SgvvPACAGeccQbXXXcd99xzD729vUfEcMYZZ3DTTTfxs5/9jIMHD5KcfOwtCJYgElhNYxsHWjqZOTHzrW0nluTx9417IxiVMdFrx44d+Hw+JkyYcNj2r371q5xzzjls2rSJRx99NKzxBikpKW91PfX5fPT09Ax7/C9/+Uu+9a1vUV1dzUknnUR9ff1h+2+++Wbuvfde2tvbOeOMM6isPPbqYksQCezl7fXMmZJN0oD+0SeU5PGvNxvo7DnyF4oxiayuro5PfepT3HDDDUeMKWhqamLq1KkA/Pa3v31r+xlnnMHDDz8MwObNm9m4ceOI1zn99NNZtmwZAA8++CBnnnkmANu3b2fx4sUsWbKEwsJCqqurDztv+/btzJs3jy996UucfPLJliDMsXlp+wGOm5R92LbMtGSKcv2sr26KUFTGRI/29va3urmed955XHDBBXz9618/4rgvfvGL3HLLLZxwwgmHlQQ+/elPU1dXx+zZs/nKV77CnDlzyMnJGfaaP//5z/nNb37D/Pnzuf/++/npT38KwBe+8AXmzZvH3LlzOf3001mwYMFh5/3kJz95qxorJSWFiy666Jg/v6jqMb9JNFi0aJHagkGj864fPMt/vms6pcGMw7Y/9OouphVk8D/nz4pQZMaMvy1btvCOd7xjTN+zt7eX7u5u0tPT2b59O+eddx5bt24lNTUyXclDfUYRWaOqIfvK2jiIBNXc0U1tcydFeYEj9s2amMUL2+oiEJUx8aWtrY1zzjmH7u5uVJW77rorYsnhaFiCSFCbapooL8jAl3Tk/CwzJ2byi+e30dunIfcbY8KTlZUV00shWxtEgqrY00xp8MjSA0B2egq5/lTeqD32gTbGmNjlaYIQkQtFZKuIbBORm0PsP0tEXhORHhG5fMD2hSLysohUiMgGEfmwl3Emos17m5iaN/RAmhkTMtlgDdXGJDTPEoSI+IA7gYuA2cBVIjJ70GG7gOuA3w/a3gZ8TFXnABcCPxGRXK9iTUSVew9REqL9oV9pMMC66oPjF5AxJup4WYI4BdimqjtUtQtYBlw68ABVrVLVDUDfoO2vq+ob7vM9QC1Q6GGsCaWnt48dB1opzh86QUwryGR9zcHxC8oYE3W8bKSeCgwcyVEDLB7tm4jIKUAqsD3EvuuB6wFKSkqOLsoEVN3YTm4ghfQU35DHlAYDbKttobu3jxSfNVUZM9Dp//s0e5rGbu3qKTnpvHTLuUPu9/l8zJs3D1XF5/Nxxx13cPrpp4/Z9YcS1b2YRGQycD9wrar2Dd6vqncDd4MzDmKcw4tZ22tbmJo7dOkBID3Fx4TsNLbXtXD8oMF0xiS6PU0dPPQfp47Z+111z7+G3T9w2vEVK1Zwyy238Pzzz4/Z9Yfi5U/D3UDxgNdF7rawiEg28BjwZVUd/u6ZUdlW18LknPQRjyvNz6Bid/M4RGSMCVdzczN5eXnjci0vSxCrgJkiUo6TGK4Erg7nRBFJBf4C/E5VH/EuxMT0+v5DYSWI4vwAm/Y08cGTisYhKmPMUPqn/Ojo6GDv3r0888wz43Jdz0oQqtoD3ACsALYAD6tqhYgsEZFLAETkZBGpAa4AfiUiFe7pHwLOAq4TkXXuY6FXsSaa7bUtTM4dea740vwAm3ZbV1djIq2/iqmyspInnniCj33sY4zHNEmetkGo6uPA44O2fW3A81U4VU+Dz3sAeMDL2BLZzvq28KqYggFe39+Cqh4xe6UxJjJOO+00Dhw4QF1d3RHTjo81656SYJrau+ns6SPXnzLisTn+FATY39zpfWDGmLBUVlbS29tLMBj0/FpR3YvJjL2qA61MyU0Pq0QgIpQVZLBlbzOTwihxGJMopuSkj9jzaLTvN5z+NggAVWXp0qX4fEN3Ux8rliASTFV966i+7Ivy/FTuO8Q5x3tblDUmlgw3ZsELoZYYHQ9WxZRgqg60MiEzLezji/ICVOyxhmpjEpEliASz40ArhdnhlyBK8gNU7rVZXY1JRJYgEszO+jYmjiJBTM31s6uxja6eIwayG2PinCWIBLOroY2JWeFXMaUmJzHRnXLDGJNYLEEkkNbOHlo6e8jLGN2ShyX5Abbus2omYxKNJYgEUt3YxsTsNJJGOeitKDfA5r02J5Mxica6uSaQ6oZ2JmaNfjxDUb6fV3Y0eBCRMTHqx3OguWbs3i+7CG6qGHK3iHDNNdfwwAPOBBM9PT1MnjyZxYsX87e//W3s4hjEEkQC2dXQRsEo2h/6leYHuP/lnR5EZEyMaq6Ba8fwi3npxcPuzsjIYNOmTbS3t+P3+3nyySeZOnXq2F1/CFbFlEB21rdSOIoxEP0KMtNo7eyhsbXLg6iMMeF473vfy2OPPQbAQw89xFVXXeX5NS1BJJCd9W0UHkUJQkQoCWZQaQ3VxkTMlVdeybJly+jo6GDDhg0sXjzqBTpHzRJEAqluaGPCUSQIgOI8P5X7rKHamEiZP38+VVVVPPTQQ7z3ve8dl2taG0SCUFX2NLUz4SgaqcGZk6lijyUIYyLpkksu4fOf/zzPPfcc9fX1nl/PEkSCONDSRWpyEv7Uo5sBsjSYwcOrq8c4KmPMaHz84x8nNzeXefPm8dxzz3l+PUsQCaK6se2ourj2K84LsL2uhd4+xZdkiweZBJddNGLPo1G/XxiKioq48cYbx+66I7AEkSCqG46ugbqfP9VHXiCVqvpWphdmjmFkxsSgYcYseKGl5cipbs4++2zOPvtsT69rjdQJoqaxnYLM0U2xMVhJfoAtNqLamIRhCSJB7KxvoyDz2FaFK8rzs9kaqo1JGJYgEsSuhtaj7uLarzQ/g027bfEgE79UNdIheOZoPpsliARR09h+7AkiGLDBciZupaenU19fH5dJQlWpr68nPX10tQjWSJ0AevuU/c0dRzUP00CFWc6UGw2tXeSPcspwY6JdUVERNTU11NXVRToUT6Snp1NUFF5vqX6eJggRuRD4KeAD7lXV7w7afxbwE2A+cKWqPjJg37XAV9yX31LVpV7GGs/2NXeQ7U8hxXdsBUYRoawggy17mzljRsEYRWdMdEhJSaG8vDzSYUQVz6qYRMQH3AlcBMwGrhKR2YMO2wVcB/x+0Ln5wNeBxcApwNdFJM+rWONddcOxjYEYqDg/YA3VxiQIL9sgTgG2qeoOVe0ClgGXDjxAVatUdQMweMHj9wBPqmqDqjYCTwIXehhrXDvWMRADleQF2GgN1cYkBC8TxFRg4NwMNe62MTtXRK4XkdUisjpe6w3HQnVDG8FjHAPRr6wgw0oQxiSImO7FpKp3q+oiVV1UWFgY6XCi1s76tqNaByKUojw/1Y1tdHT3jsn7GWOil5cJYjdQPOB1kbvN63PNIDuPYZrvwVJ8SUzJ9fP6fuvuaky88zJBrAJmiki5iKQCVwLLwzx3BXCBiOS5jdMXuNvMUdjd2E7hGDVSg7MEqVUzGRP/PEsQqtoD3IDzxb4FeFhVK0RkiYhcAiAiJ4tIDXAF8CsRqXDPbQC+iZNkVgFL3G1mlDq6e2lq7yY4huMWivOtodqYRODpOAhVfRx4fNC2rw14vgqn+ijUub8Gfu1lfImgprGdwqw0ksZwiu6yYIBH1+8ds/czxkSnmG6kNiOrbmhjYvbYtD/0Kwlm8HrtIfr64m9KAmPM2yxBxLldDWPXg6lfZloy2enJVNW3jun7GmOiiyWIOLeroY3gGCcIcJYg3WxrQxgT1yxBxLkddS1MzB67Hkz9SvIDNvW3MXHOEkSc2+VBGwRASTBAhXV1NSauWYKIY6rqrgMx9iWIsmCGLT9qTJyzBBHHag91Ekj14U/1jfl7BzNS6ezpo+5Q55i/tzEmOliCiGM769uY5EH7AzhrQ5QXWEO1MfHMEkQcq6pvZYJHCQKgKC9g1UzGxDFLEHHszbrWMZukL5SSfL/1ZDImjlmCiGM7DnjTxbVfSb6tDWFMPLMEEceqDrQxKcfLKiY/NQfbbW0IY+KUJYg4papUN3rXSA3u2hA56WyrbfHsGsaYyLEEEadqD3WSlpxERpqnE/ZSkm8N1cbEK0sQcWp7XQtTcv2eX2dqXsC6uhoTpyxBxKkdda2eVi/1K7HV5YyJW5Yg4pRXk/QNVpIfsPWpjYlTliDi1Bu1LUz2sAdTv7xACr19alNuGBOHLEHEqe21LUzN874NQkQoDWZQuc+qmYyJN5Yg4lB7Vy8HWrs8mcU1lKI8P1v3WTWTMfHGEkQc2l7XwtRcP74kGZfrTc31W08mY+KQJYg4tK22hSm541N6ACjOD1BpJQhj4o4liDhUua95XMZA9CvK87OjroXePh23axpjvOdpghCRC0Vkq4hsE5GbQ+xPE5E/uPtfEZEyd3uKiCwVkY0iskVEbvEyznizZe8hinMD43a9QGoyOf4Uqhvaxu2axhjveZYgRMQH3AlcBMwGrhKR2YMO+wTQqKozgNuB77nbrwDSVHUecBLwyf7kYUb2xv5DFOWPXwkCoDgvwFYbD2FMXPGyBHEKsE1Vd6hqF7AMuHTQMZcCS93njwDniogACmSISDLgB7oAawUNQ0tnDw1tXUwcpx5M/abmWk8mY+KNlwliKlA94HWNuy3kMaraAzQBQZxk0QrsBXYBP1TVhsEXEJHrRWS1iKyuq6sb+08Qgyr3NlOan0HSOPVg6jc1z3oyGRNvorWR+hSgF5gClAOfE5Fpgw9S1btVdZGqLiosLBzvGKPS5r3NlATHt3oJnOVH37AqJmPiipcJYjdQPOB1kbst5DFudVIOUA9cDTyhqt2qWgu8CCzyMNa4sbGmieK88Wug7jc11091YztdPX3jfm1jjDe8TBCrgJkiUi4iqcCVwPJBxywHrnWfXw48o6qKU630bgARyQBOBSo9jDVubNrdRGkwY9yvm5qcRGFmGlX1reN+bWOMN8JKECLyZxF5n4iEnVDcNoUbgBXAFuBhVa0QkSUicol72H1AUES2ATcB/V1h7wQyRaQCJ9H8RlU3hHvtRNXZ08ub9a2URSBBABTn+21mV2PiSLjLjd0F/D/gZyLyR5wv7K0jnaSqjwOPD9r2tQHPO3C6tA4+ryXUdjO8yr2HmJLjJzU5Mk1Lk3P8vL7vEMyPyOWNMWMsrG8SVX1KVa8BTgSqgKdE5CUR+X8ikuJlgCZ8G2oOUlYw/u0P/Yrz/GyxWV2NiRth/9QUkSBwHfDvwFrgpzgJ40lPIjOjtrqqkWmFmRG7flFegNf3t0Ts+saYsRVuG8RfgJVAAPg3Vb1EVf+gqv8FRO4byRzmtV2NzJqQFbHrT85JZ29TB509vRGLwRgzdsJtg7jHbU94i4ikqWqnqlr30yjQ0NpFQ2vXuCwSNJRkXxKTstPZXtvK7CnZEYvDGDM2wq1i+laIbS+PZSDm2KyqauC4SVkkyfiOoB6sKM/PG7XWk8mYeDBsCUJEJuFMh+EXkROA/m+fbJzqJhMlXn2zgZkTI1e91G+KzclkTNwYqYrpPTgN00XAjwdsPwTc6lFM5ii8vKOeK04sinQYFOX6WV9zMNJhGGPGwLAJQlWXAktF5IOq+qdxismMUlN7N1UHWpk+IfL9BYryA/xxTU2kwzDGjIGRqpg+oqoPAGUictPg/ar64xCnmXH2yo56jpuYRYov8nMvTspOp66lk7auHgKp4faBMMZEo5G+UfrnbMgEskI8TBR4YdsBjp8cHf85fElCUa6fbbU2HsKYWDdSFdOv3L/fGJ9wzNF4fmsdn3zX9EiH8ZaiPKehen5RbqRDMcYcg3AHyn1fRLLdtaKfFpE6EfmI18GZkVU3tNHU3k1pMHo6lU3J9VNpPZmMiXnhVlpfoKrNwMU4czHNAL7gVVAmfM+/Xsf8opyIj38YqDg/wBZbXc6YmBduguivinof8EdVbfIoHjNKT1fuj7qqnOK8gE37bUwcCDdB/E1EKoGTgKdFpBDo8C4sE47Onl5efbOBeUU5kQ7lMAWZqbR39dLY2hXpUIwxxyDc6b5vBk4HFqlqN9AKXOplYGZkr77ZQHFegOz06JpxXUQoCWZYO4QxMW40HdWPxxkPMfCc341xPGYUnt6ynwVRVr3UrzjPz9Z9zZw2PRjpUIwxRymsBCEi9wPTgXVA/1zOiiWIiHqmso7/PDt6urcOVJTnp2KPNVQbE8vCLUEsAmarqnoZjAnfmwdaae3soTQ/erq3DlSSn8Eja6ojHYYx5hiE20i9CZjkZSBmdJ7bWsuC4hwkirq3DlSc72dbXQu9ffabwphYFW4JogDYLCKvAp39G1X1Ek+iMiN6eksti0rzIh3GkAKpyeT6U6iqb2V6BJdBNcYcvXATxG1eBmFGp6O7lzW7Grnu9LJIhzKs0mAGW/Y2W4IwJkaF2831eZwR1Cnu81XAax7GZYaxuqqR0vwAGWnRPVtqUZ6fzdZQbUzMCncupv8AHgF+5W6aCvw1jPMuFJGtIrJNRG4OsT9NRP7g7n9FRMoG7JsvIi+LSIWIbBSR9HBiTQTPv17H3BhY87k0P8N6MhkTw8JtpP4McAbQDKCqbwAThjtBRHzAncBFwGzgKhGZPeiwTwCNqjoDuB34nntuMvAA8ClVnQOcDXSHGWvce2FbHXOmRtfo6VDKCgJWgjAmhoWbIDpV9a15E9wv8JG6p5wCbFPVHe65yzhy9PWlwFL3+SPAueJ0y7kA2KCq6wFUtV5VezEcbOtiZ30bM2KgXr8gM4327l4OtHSOfLAxJuqEmyCeF5FbAb+InA/8EXh0hHOmAgM7wte420Ieo6o9QBMQBGYBKiIrROQ1EflimHHGvX/taOD4SVkkR8HqcSMREcoLMqwUYUyMCvdb5magDtgIfBJ4HPiKV0Hh9K56J3CN+/cyETl38EEicr2IrBaR1XV1dR6GEz1e3n6A4yZFx+px4SjJD1g7hDExKtxeTH04jdKfVtXLVfWeMEZV7waKB7wucreFPMattsoB6nFKG/9U1QOq2oaTkE4MEdfdqrpIVRcVFhaG81FinlOCiP4G6n6lwQAbag5GOgxjzFEYNkGI4zYROQBsBba6q8l9LYz3XgXMFJFyEUkFrgSWDzpmOXCt+/xy4Bk38awA5olIwE0c7wI2h/+x4lNTeze7GtqYVpAx8sFRoixoPZmMiVUjlSA+i9N76WRVzVfVfGAxcIaIfHa4E902hRtwvuy3AA+raoWILBGR/hHY9wFBEdkG3IRTlYWqNgI/xkky64DXVPWxo/mA8eS1XY3MnJgZE+0P/abk+qk71EFzh3VCMybWjDTS6qPA+ap6oH+Dqu5w16P+B07X1CGp6uM41UMDt31twPMO4Iohzn0Ap6urca16syEmei8N5EsSpxSx26b+NibWjPRTNGVgcuinqnVAdK1SkwBWVTUwc2LsNFD3Ky3IYNNuW6XWmFgzUoIYbs1IW09yHPX09lGxp5mZE2KrBAFQFgywtrox0mEYY0ZppCqmBSISqoVRAJv6YhxV7jtEQWZa1M+/FMq0gkwe37gv0mEYY0Zp2G8bVfWNVyBmeGt3NTJjQuz0XhpoYEN1tK2fbYwZWux0h0lwa3Y2Mi3GGqj7+ZKEsoIMNtVYO4QxscQSRIxYV30w5nowDVQWzGCjNVQbE1MsQcSApvZu9jd3UJQXnetPh6O8IIO11QcjHYYxZhQsQcSAjTVNTCvMxJcUnetPh2NaYSYbrYrJmJhiCSIGrK9ppCwYmw3U/SbnpHOwrYvGVusdbUyssAQRA9ZVH6Q8huZfCiVJxClFWDuEMTHDEkQM2FDTxLTC2E4Q4AyYW28zuxoTMyxBRLm6Q520dvYyKTv2xyWWF2SwdtfBSIdhjAmTJYgot2l3EzMmZOKsxBrbphVm2pxMxsQQSxBRbn31QUrzY7d760ATstJo6+ql7pCtUW1MLLAEEeXW1RykPA7aH8BZo3rGBCtFGBMrLEFEuU27m5hWELsjqAcrDQZYbwPmjIkJliCi2P7mDrp6+ijITI10KGOmPJjBOuvJZExMsAQRxTbUNDG9MD4aqPtNK3RWlzPGRD9LEFFsQ/VBymJ8gNxgBZlpdPb2UtvcEelQjDEjsAQRxdbVxP4I6sFEhOkFmWzaYw3VxkQ7SxBRSlXdBur4ShAAJfkBm7jPmBhgCSJK7T7YjoiQnxE/DdT9ygoyWG8JwpioZwkiSq2vbmJGYUZcNVD3Ky/IsLEQxsQATxOEiFwoIltFZJuI3Bxif5qI/MHd/4qIlA3aXyIiLSLyeS/jjEbrqw9SGofVS+CMqG7t6qG+xUZUGxPNPEsQIuID7gQuAmYDV4nI7EGHfQJoVNUZwO3A9wbt/zHwd69ijGZrqxvjaoDcQCJCeUEGFXusu6sx0czLEsQpwDZV3aGqXcAy4NJBx1wKLHWfPwKcK26dioi8H3gTqPAwxqjU26dU7GmO6TWoR1Kan2E9mYyJcl4miKlA9YDXNe62kMeoag/QBARFJBP4EvAND+OLWttqW8gLpJKZnhzpUDxTGrSeTMZEu2htpL4NuF1VW4Y7SESuF5HVIrK6rq5ufCIbB+uqG5keJxP0DaUsaFVMxkQ7L3+i7gaKB7wucreFOqZGRJKBHKAeWAxcLiLfB3KBPhHpUNU7Bp6sqncDdwMsWrRIvfgQkbC6qpHpcVy9BDA5N539zR20dPaQmRa/JSVjYpmXJYhVwEwRKReRVOBKYPmgY5YD17rPLweeUceZqlqmqmXAT4DvDE4O8WztroPMmBDfCSI5KYmS/ACVe60UYUy08ixBuG0KNwArgC3Aw6paISJLROQS97D7cNoctgE3AUd0hU00zR3d7D7YTkkwPhYJGk5pMMBmSxDGRC1Py/aq+jjw+KBtXxvwvAO4YoT3uM2T4KLUul0HmV6YQXJStDYPjZ2S/AAbrKHamKgV/99CMWbNzgamx3n1Ur/SYAYVNqLamKhlCSLKvPJmA7MmZEU6jHFRkh9gx4FWunv7Ih2KMSYESxBRpKe3jw01TcyamBgJIj3FR2FWGtvrhu3NbIyJEEsQUWTz3mYmZKXF9QC5wcqCATbbeAhjopIliCjy6psNCVN66FecF7CZXY2JUpYgosiL2w9w3KTEShClwQw2WoIwJipZgogSfX3KmqpG3jE5O9KhjKvSYICt+w6hGjcD4Y2JG5YgosSWfc1k+1PIC8TfCnLDyQ2kkuJLYvfB9kiHYowZJHFaQ6Pcy9vrmZ1gpYd+/WtDFOWN8ehxVejtgu426O0G7QMEfCmQ4ofkdIjDFfuMGSuWIKLEyjcOsKAoN9JhRERxfoCK3U28Z86k8E5oPwiNVdBUDc17nMehfdBaB20HnP0dTdDldp9NTneSgiQ5SaOvB3o6nISRngMZhZA9BXJLIX8aBGdAwSzIL4ckn0ef2pjoZwkiCnT39rFmZyNXLy6JdCgRURoMsK76YOidbQ2w80XY9S/Y8xrUVjpf7tlTIbMQ/EHw5zpf8sGZ4M+B1CxIzXAevpShL9zbDZ2HoL3RSSwttbB7DVT+DQ7ucrbnT4OJc2DSPOfvxLmQOdFKHiYhWIKIAhtqDjIxO43s9GG+zOJYWTCD37+y6+0N7Y2w8RHY8DDsr4BJc6DgOJj5Hjj5eggEx+YL2pcCgXznEZx+5P7uDji403nUrIGKv0DDDqckMuEdMGm+kzgmzYPC4yE57dhjMiaKWIKIAv98/UDCtj8ATMhKo62rl/rd2wm+9jPY+CcoWgSzLoSzbwZfhBruU9Kh8Djn0U8V2hucKq7GN2HTn2Hlj5xqrvzpTtwlp0HZGZCbmCVCEz8sQUSB51+v46K5Yda/xyHpaWdaaiObfv1p3jWnFN7/C6faKBqJOCWYQBCmnvT29p5OJ2HUbYX1D8GKWyEtE2ZeALMvhZLTwWf/3Exssf9jI6y5o5ut+w7x2fNmRTqUyNizFl78CSXyHjbNvIF3nZAb6YiOTnKaU81UeLzzWtVJGLtXw2OfcxrOF1wFJ38C8kojGqox4bIEEWEvbTvA8ZOySE1OsCEp2guvPQDb/gGzP0BZVznr6uNoVlcRp4E7fxrM+5DT6L3tKfjVWVB+FrzrSzBpbqSjNGZYCfatFH2eqaxj7tScSIcxvrrb4Olvwp41cOpnoHAm03KS2HSgN9KReSe3BBZ9HD5wj9Pj6neXwCMfh4PVkY7MmCFZgoggVeX512uZX5RACaKjCf5+s/ML+8TrnHp6YGKG0NylNLTHUSkilBQ/zLnMaWfxpcIv3wkrb4fenkhHZswRLEFE0Bu1zkCuqbn+CEcyTtoPwt+/CHklMPv9hzXaJokwLTeJDXVxniD6pQRg4TVw0Q9gy3K45xyoez3SURlzGEsQEfRsZS0LinKRRBh01dkMT9zsNOLOvCDkOIby7CQ21CXYL+nsyXDeN6DsTPj1BbBmaaQjMuYtliAi6JnKWuYlQvVSTzs8+TWnwXb6u4c8bFpuEq/tj+N2iKGIwHEXwQXfgRd+DH/5lDNIz5gIswQRIYc6utlQ08TcKXGeIPp64dnvQHouzHrPsCOgp7tVTAk79XduMVz0Q2fQ3W8udOaXMiaCLEFEyIvbnMWB0lPifDK4V+92Js2bfemI02ME/c7+3S0JmiDAGb195uedeZ/ueTfs3xzpiEwCswQRIU9tToDeS1v/DjWrYP6VYc2KKiLMzEtibSJWMw0k4tyzBVfBb98Hb66MdEQmQXmaIETkQhHZKiLbROTmEPvTROQP7v5XRKTM3X6+iKwRkY3u36ErrmNQX5/y7NZaTijOi3Qo3qndDK8thYUfcbp2hmlabhJr9idYQ/VQpp0NZ34OHv4obHk00tGYBORZghARH3AncBEwG7hKRGYPOuwTQKOqzgBuB77nbj8A/JuqzgOuBe73Ks5IqNjTjD/Vx6Sc9EiH4o32BqfdYe4HILNgVKfOzEti1b4EL0EMNHkBnPt1ePS/Ye2DkY7GJBgvSxCnANtUdYeqdgHLgEsHHXMp0N+v7xHgXBERVV2rqnvc7RWAX0TiZi7lp7bsj9/Fgfp64bnvOhPZ9c9LNArTcpLY3thHW3cCt0MMFpwBF3wLnroNXr030tGYBOJlgpgKDJxHoMbdFvIYVe0BmoDgoGM+CLymqp2DLyAi14vIahFZXVdXN2aBe+2pLftZWJwb6TC8sfYB6OuGaecc1elpyUJZThLraq0UcZicYnjPd2DlD+GlOyIdjUkQUd1ILSJzcKqdPhlqv6reraqLVHVRYWHh+AZ3lGoPdbCzvo3jJ2dFOpSxt3uNM/ne3Csg6ej/15qZl8SqvdYOcYSsSXDBt+FfdznTcxjjMS8TxG6geMDrIndbyGNEJBnIAerd10XAX4CPqep2D+McV87o6RySj+ELNCq11TuDvOZd8db8Skfr+KCPl/ZYCSKkzAlOklh9H/zzh5GOxsQ5L7+lVgEzRaRcRFKBK4Hlg45ZjtMIDXA58IyqqojkAo8BN6vqix7GOO5WVOxnQbxVL2kvPP99KDrZGS19jI7PT2JDXS+dvdYOEVJGgdMm8dpS574b4xHPEoTbpnADsALYAjysqhUiskRELnEPuw8Iisg24CagvyvsDcAM4Gsiss59TPAq1vHS0d3LK2/Wx1/7w/pl0NMB5WePydsFUoTirASddiNcgSCc/y2nzee570Y6GhOnPF0wSFUfBx4ftO1rA553AFeEOO9bwLe8jC0SXtx2gPJgBlnpKZEOZezs2wiVf3PWdRjDarPZwSReqOnhtCm2ptWQAvlOSeLJr0JfD5zz5RFHqxszGnFWER7dVlTs54SSOBoc19EE//w+zPkApGeP6VvPLfTxXLU1VI/In+eUJDb9yekGm6jzWBlPWIIYJ719ylNb9nNSaZwkCO1zulxOnAeFx43528/KS6KqqY/6eF9AaCz4c+H8b0LlY/D3L1mSMGPGEsQ4WburkWx/MhOz42T09KY/QVsDzDzfk7dP8QlzCnysrLF2iLCk58AF34SqF+D/PuMMWDTmGFmCGCd/37SPk+Klemn/Jtj0Z5j/4bAm4Tta8wt9/KOq27P3jzupmXDebVBbCX+8FnqOGFtqzKhYghgHqsrjG/dycll+pEM5du2NTq+ZuR9wqjY8dNJEHytreqy762ik+OHdX3GWd73/MqedyJijZAliHGzc3YQIlOQHIh3KsenrcSbhm3qiJ+0Og+WmC0VZSby02xqrR8WX4qwp4c+He8+Hg9Ujn2NMCJYgxsHy9XtYXB6M/bWnV93rNIBOG7/Z10+e5GP5NqtmGrUkH5xyPZSfBfeeC9WvRjoiE4MsQXisr095dP0eTp02eA7CGPP6Cqh+xZlKYxynCVk8xcdTO3vo6LFqplETcVbyO+WT8PsPwZqlI59jzACWIDz2alUD/hRfbFcv7dsIa34DC6+B1PAX/xkL+elJTMtN4qmdVs101IpPcWaCfeHH8JdPQVdrpCMyMcIShMceWVPDadNjuPTQtAue+w7M+5AzUVwEnDE1mYe2dEXk2nEjpxgu+gG01MEvz4Q96yIdkYkBliA81NbVw4pN+3jnjNiYivwIrXXwj6/CzPdAwYyIhbF4so+Ndb1UN9uguWOS4od3/g/MuQzufz888x3rCmuGZQnCQ3/bsJfjJmeRn5Ea6VBGr6MJ/vFlKDrF6bUUQak+4ayiZJZW2JfZmJh2NrzvdqhaCXedBtufjXREJkpZgvCIqrL0pSrOOS4GJ6HtaIInboGC46D8zEhHA8D55ck8XNnNoS5rrB4TGQVw9i2w4Cr466fhwSugdkukozJRxhKER17b1UhjaxcLY23t6fYGeOJLzroOM86LdDRvmRBIYn6hjwesFDF2RKDkVLj0Dsgtgd9cBA9/DPZuiHRkJkpYgvDIL5/bwQVzJpGUFENjH5p3w2NfgMLZzhxLUTZu45IZKdy9oZsWK0WMLV8qzH4/XHa3s87EAx+EX1/oTKdibRQJzRKEB7buO8TqnQ2cfVwMNU7vr4DHvwClp8H0s6MuOQAUZycxryCJX66zLy1PpPidqdsv+xWUnQkv/Rx+OAuW/5fTTtFjPckSja3G4oHv/n0LF8+fQlqydxPZjR11pole+yDM/SAUzop0QMO64rgUvryygw+/I5XiLPt94wlfijMCu/wsaKmFnS/AilugaTeUngEzzoWydzptVPG2tro5jCWIMbbyjTq27D3EdaeXRzqUkXU2w4s/c8Y6nHI9ZET/eI2CQBLvnZbCrc+387v3BWJ/+pJolznBKVXM+YAzUeOedfDGP+CF26HzEExeAFNOhMnzYcJsCM6A5BjstWdCsgQxhtq6erj1zxv56KmlpCZH8y8rhZ0vwb9+ARPnOFMx+GJnGdT3TU/mthc7eXBzFx+ZkxbpcBKHPw+mn+M8wJkx9sDr0LAd1vwWDu6EQ/sgp8hJFAWznL/B6ZA/HbImW4kjxliCGCOqylf/uonphZmcGM2rxjXtglfvcaoL5l0B+TFQ0hkkOUn4zAmpfOOlDuYWJrNwQixU5cUhf64zjUfxKW9v6+12Ojs073H+vv4ENO+F5hpnio/cYidZBGdCwUw3gcxwSipWGow6liDGyL0r32R1VSO3XTIn0qGEdmgPrP8D7PqXU7c8+zLwxe5//smZSfz7/FT+/Yk2/nBJgOm5liSigi8F8sqcx2Dd7XBor5s89kDl35zk0bTLWQEvf5pT6ig83mkLK5jlbEu2UmKkxO43RBS5b+UO7n1hB19532zSU6Lpi0ph3ybYvBz2bXD6vL/zpnGfcM8riyYl09oFH17exn0XBlhgJYnoluJ3vvDzpx25r6PZKXE0VUNdJex4xinlHtoLWZOcEsfAxBGc6Qz2s1KHp0TjZIHzRYsW6erVq8f1mq2dPXx9eQWv7KjnC+85jsKsaFhvWp0FYqpegO1PAwLFi5yGxORoiG/srdrbw30bu/j4vFT+Y34agRT70ogbvd1Ou0ZzDTTVvF0CObgLUMgrd6uq3Oqq/GlOtak/z5JHmERkjaouCrnPywQhIhcCPwV8wL2q+t1B+9OA3wEnAfXAh1W1yt13C/AJoBe4UVVXDHet8UwQ3b19/N+6PfxgRSWzJ2fz0VPL8KdG8NdrRxPUVjg9TGpWQ2+X06Nk8gKnwTAB/qHUtfWxbEs3FfW9nF+awkmTfEzNTCIzFXwi+JIg3QfZqUK+X0iOpQGMJrSOprerqw7tcRLJoX1OSUTEmcE2txTySp2R4tlTnUfWRMicaFVXrogkCBHxAa8D5wM1wCrgKlXdPOCYTwPzVfVTInIlcJmqflhEZgMPAacAU4CngFmq2jvU9bxOEL19SsWeJp7YtI9H1tQwISuND55UxPGTsj275hG6WqBlHzTtcYriDTucHiSdLc4/hPwyCM5yiuQJkBRCqWvrY83+XnY19dHQoXT0QK9Cn0J3n9LSDS1dyuQMYVa+jwWFSSyYkMy8wiTy0q2HTVxQdbrgtux3H7XQdsCZRqa1HtrqnecpAQjkO0uzBvIhPddpeE/PhbQsSMuE1CxIDTjHpmY4f1MC7jY/pGQ47S4x/O9tuAThZRvEKcA2Vd3hBrEMuBTYPOCYS4Hb3OePAHeI07H9UmCZqnYCb4rINvf9XvYi0K6ePpav38PBti46e/po7+qluaOb+pYuqhvb2FHXSkuns2DN3CnZXHbC1LcWANpR1xL6TbtbYfdrznNVoA/6+kD7AHWf94L2QG+P86u/r9uZ2qC7E7rb3EfrkdMdpGU7RWh/Hky80PmL+z9oM9C8f6xvUUyZlQKzCobe39MH+zt87Gr18cQbPu5am0x7r3P/8lP7KMnsZYq/l2B6H9kpSsCnpPqUtCS4tKSDnNT4qJaNeyLOj6WsSUfu0z7nh1Vnk5NM+h/1253G9O526GmHng73eQd0dzj/RsPhS3UTiJtQUvzOI7n/b5rz8KU540Z8qU6iSUqBpGT34XMekgTicz6PJAHu34FJacI7nMGLY8zLBDEVGLhaeg2weKhjVLVHRJqAoLv9X4POnTr4AiJyPXC9+7JFRLaGiKMAODBcoJKS7k8JFs0e7hjt6+2mr7dnD/CP4Q50+QRfqo9RjxjqamshNZB55PWVqG0t6m5vIcV/ZMzRaqh4e0iWvZJExTDnfrWvtitLW4YsyXqhoV2T8/0SM0vqxVq8cGwxiyBJQlISJImQlCTeT2FU16YUBt5OEL1K77p9feuO8u1Kh9oR072YVPVu4O7hjhGR1UMVn6KRiKxub6qPmXjBibkjhmKOxXj3HOqzeD0UazGLyOqdB72P18tMtxsoHvC6yN0W8hgRSQZycBqrwznXGGOMh7xMEKuAmSJSLiKpwJXA8kHHLAeudZ9fDjyjTqv5cuBKEUkTkXJgJvCqh7EaY4wZxLMqJrdN4QZgBU4311+raoWILAFWq+py4D7gfrcRugEnieAe9zBOg3YP8JnhejCNYNgqqCgUa/FC7MVs8Xor1uKF2It5XOKNm4FyxhhjxpZ1/DbGGBOSJQhjjDEhxXWCEJEqEdkoIutEZHwnagqDiPxaRGpFZNOAbfki8qSIvOH+jZq5w4eI9zYR2e3e43Ui8t5IxjiQiBSLyLMisllEKkTkv93t0XyPh4o5Ku+ziKSLyKsist6N9xvu9nIReUVEtonIH9yOKhE3TLy/FZE3B9zfhREO9TAi4hORtSLyN/f1uNzfuE4QrnNUdWGUjoX4LXDhoG03A0+r6kzgafd1tPgtR8YLcLt7jxeq6uPjHNNweoDPqeps4FTgM+40LtF8j4eKGaLzPncC71bVBcBC4EIRORX4Hk68M4BGnHnVosFQ8QJ8YcD9XRepAIfw38CWAa/H5f4mQoKIWqr6T5zeWwNdCix1ny8F3j+eMQ1niHijlqruVdXX3OeHcP6BTSW67/FQMUcldfTPN5PiPhR4N870ORBF93iYeKOWiBQB7wPudV8L43R/4z1BKPAPEVnjTssRCyaq6l73+T5gYiSDCdMNIrLBrYKKmuqagUSkDDgBeIUYuceDYoYovc9u9cc6oBZ4EtgOHFTV/qkrQk6VEymD41XV/vv7bff+3u7ONB0tfgJ8EehzXwcZp/sb7wninap6InARTlH9rEgHNBruoMGo/nUD/AKYjlNc3wv8KKLRhCAimcCfgP9R1eaB+6L1HoeIOWrvs6r2qupCnBkPTgGOj2xEwxscr4jMBW7BiftkIB/4UuQifJuIXAzUquqaSFw/rhOEqu52/9YCf8H5nzfa7ReRyQDu39oIxzMsVd3v/oPrA+4hyu6xiKTgfNE+qKp/djdH9T0OFXO032cAVT0IPAucBuS60+dAlE6VMyDeC92qPXVnkP4N0XN/zwAuEZEqYBlO1dJPGaf7G7cJQkQyRCSr/zlwAbBp+LOiwsDpR64F/i+CsYyo/4vWdRlRdI/dutr7gC2q+uMBu6L2Hg8Vc7TeZxEpFJFc97kfZ/2XLThfvJe7h0XNPR4i3soBPxgEpz4/Ku6vqt6iqkWqWoYz08QzqnoN43R/43YktYhMwyk1gDOlyO9V9dsRDOkIIvIQcDbOlOT7ga8DfwUeBkqAncCHVDUqGoaHiPdsnGoPBaqATw6o348oEXknsBLYyNv1t7fi1OlH6z0eKuariML7LCLzcRpJfTg/OB9W1SXuv79lONU1a4GPuL/OI2qYeJ8BCnEWVlkHfGpAY3ZUEJGzgc+r6sXjdX/jNkEYY4w5NnFbxWSMMebYWIIwxhgTkiUIY4wxIVmCMMYYE5IlCGOMMSFZgjBxS0R63Zk5N4nIo/3930dx/nMissh9/vhozx/iPU8QkfuO9X3CvNatA56nisg/BwyuMmZEliBMPGt3Z+acizPJ4GeO9o1U9b3uyNtjdSvwszF4HwBG+MJ/K0GoahfOzLUfHqtrm/hnCcIkipdxJzQTkVNE5GV3fv2XROQ4d7tfRJaJyBYR+Qvg7z9ZnLVFCkSkTA5fD+PzInKb+/xGcdZx2CAiywYH4I7sn6+q693XG0UkVxz1IvIxd/vvROR8cdYu+I173FoROcfdf52ILHcHdz0tIpPd0kF/aelMEfku4He3PeiG8FfgmjG+ryaOWXHTxD0R8QHn4kxhAVAJnKmqPSJyHvAd4IPAfwJtqvoOd8Tta6O81M1Auap2DlEdtYjDp3B4EWeunZ3ADuBM4Hc4cxn9J06JR1V1nogcjzMz8Sz33BNxkk2DiHwOWKGq33Y/a0BVV4rIDe6kdP024UxGZ0xYLEGYeOZ3p3WeijM/0JPu9hxgqYjMxJm6IsXdfhZu9Y+qbhCRDaO83gbgQRH5K86v9cEmA3UDXq90r7kTZ7bW60VkKtCoqq3utBs/d+OpFJGdQH+CeHLA9CCrgF+7k/z9dajFblS1V0S6RCTLXWvCmGFZFZOJZ+3uL+hSnDl2+tsgvgk867ZN/BuQPor37OHwfzcDz30fcCfOr/tVIdoH2gcd/0+cUsOZwHM4yeNynMQxktb+J+5CTmfhzOj52/6qqiGkAR1hvL8xliBM/FPVNuBG4HPul3YOb0+PfN2AQ/8JXA3grhEwP8Tb7QcmiEhQnEVlLnaPTwKKVfVZnLUEcoDMQeduAWYMiKsaZ+LDmaq6A3gB+LwbBziJ4hr3/WfhTC64dXBAIlIK7FfVe3BWHTvR3dXtlir6jwsCB1S1O8TnMuYIliBMQlDVtThVQFcB3wf+V0TWcng16y+ATBHZAiwBjlikxf1yXQK8ilNlVenu8gEPiMhGnNk1fza415OqVgI5/dPQu14BXnefr8SpDnvBfX0XkOS+5x+A64aYsfNsYL37eT6Ms14AwN3AhgGN1OcAj4U435iQbDZXY8aRiHwWOKSq90bg2n8GblbV10c82BisBGHMePsFMO7rIohIKk4DtiUHEzYrQRhjjAnJShDGGGNCsgRhjDEmJEsQxhhjQrIEYYwxJiRLEMYYY0L6/40OnzsFEqutAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Lines below will give you a hint or solution code\n#step_4.a.hint()\nstep_4.a.solution_plot()","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:52:00.170170Z","iopub.execute_input":"2022-10-31T08:52:00.170548Z","iopub.status.idle":"2022-10-31T08:52:00.358187Z","shell.execute_reply.started":"2022-10-31T08:52:00.170522Z","shell.execute_reply":"2022-10-31T08:52:00.357130Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 2, \"questionId\": \"4.1_PlotThreshold\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: \n```python\n# KDE plots for benign and malignant tumors\nsns.kdeplot(data=cancer_data, x='Radius (worst)', hue='Diagnosis', shade=True)\n\n```","text/markdown":"<span style=\"color:#33cc99\">Solution:</span> \n```python\n# KDE plots for benign and malignant tumors\nsns.kdeplot(data=cancer_data, x='Radius (worst)', hue='Diagnosis', shade=True)\n\n```"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"#### Part B\n\nA hospital has recently started using an algorithm that can diagnose tumors with high accuracy.  Given a tumor with a value for `'Radius (worst)'` of 25, do you think the algorithm is more likely to classify the tumor as benign or malignant?","metadata":{}},{"cell_type":"code","source":"#step_4.b.hint()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Check your answer (Run this code cell to receive credit!)\nstep_4.b.solution()","metadata":{"execution":{"iopub.status.busy":"2022-10-31T08:52:04.047804Z","iopub.execute_input":"2022-10-31T08:52:04.048129Z","iopub.status.idle":"2022-10-31T08:52:04.057056Z","shell.execute_reply.started":"2022-10-31T08:52:04.048106Z","shell.execute_reply":"2022-10-31T08:52:04.055567Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 4, \"questionId\": \"4.2_ThinkThreshold\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: The algorithm is more likely to classify the tumor as malignant. This is because the curve for malignant tumors is much higher than the curve for benign tumors around a value of 25 -- and an algorithm that gets high accuracy is likely to make decisions based on this pattern in the data.","text/markdown":"<span style=\"color:#33cc99\">Solution:</span> The algorithm is more likely to classify the tumor as malignant. This is because the curve for malignant tumors is much higher than the curve for benign tumors around a value of 25 -- and an algorithm that gets high accuracy is likely to make decisions based on this pattern in the data."},"metadata":{}}]},{"cell_type":"markdown","source":"## Keep going\n\nReview all that you've learned and explore how to further customize your plots in the **[next tutorial](https://www.kaggle.com/alexisbcook/choosing-plot-types-and-custom-styles)**!","metadata":{}},{"cell_type":"markdown","source":"---\n\n\n\n\n*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/data-visualization/discussion) to chat with other learners.*","metadata":{}}]}