ML Model Explorer is an interactive Streamlit web application that empowers you to explore various machine learning classifiers and optimize their hyperparameters. Experiment with popular datasets and classifiers to understand their impact on classification tasks.
Try it out live at ml-model-explorer.streamlit.app.
- Dataset Exploration: Choose from popular datasets like Iris, Breast Cancer, and Wine.
- Classifier Variety: Experiment with a diverse range of classifiers, including Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, Random Forests, Gradient Boosting, and Naive Bayes.
- Hyperparameter Tuning: Adjust hyperparameters specific to each classifier through user-friendly sliders to see how they impact model performance.
- Performance Metrics: Gain insights into model performance with accuracy, precision, recall, and F1 scores.
- Confusion Matrix: Visualize classification performance through a confusion matrix.
- Classification Report: View detailed reports analyzing class-wise performance.
- ROC Curve: For binary classification tasks, visualize the ROC curve and the area under the curve (AUC).
- Visit ml-model-explorer.streamlit.app to explore the app online.
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Clone the Repository: Clone the repository using the following command:
git clone https://github.com/your-username/ML-Model-Explorer.git
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Install Dependencies: Navigate to the project directory and install the required libraries:
pip install -r requirements.txt
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Run the App: Launch the application in your web browser with the command:
streamlit run main.py
We welcome contributions to this project! To contribute, follow these steps:
- Fork the repository.
- Create a new branch for your changes (
git checkout -b feature/your-feature). - Make your modifications and commit them with descriptive messages (
git commit -m 'Added new visualization feature'). - Push your changes to your branch (
git push origin feature/your-feature). - Submit a pull request for review.
For more detailed guidelines, see our Contributing Guidelines.
This project is licensed under the Apache License - see the LICENSE file for details.
Please review our Code of Conduct before contributing.
For questions, feedback, or contributions, please open an issue in the repository or reach out directly.
Happy exploring!