Explore and visualize machine learning models with ease using this ML Model Visualizer! The ML Model Visualizer is an interactive Streamlit application designed to explore different datasets and machine learning models. It allows users to visualize the effects of hyperparameter tuning on both classification and regression models. Users can select datasets, choose from a range of models, and immediately see how changes in hyperparameters impact model performance. This tool helps in understanding how different settings influence model outcomes and aids in selecting the best configuration for a given problem.
The ML Model Visualizer lets you:
- Choose from 7 Downloadable Datasets: Access and experiment with diverse datasets suitable for classification and regression tasks.
- Visualize 20+ Models: Includes models such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest, and more. Detailed documentation links are provided for each model.
- Tune 100+ Hyperparameters: Fine-tune models with a wide range of hyperparameters and perform cross-validation to optimize model performance.
- Visualize Model Performance: Use various visualization techniques including decision boundaries, tree plots, regression plots, heatmaps, and more to understand model behavior.
- Display 10+ Metrics: Evaluate model performance with multiple metrics such as accuracy, precision, recall, and F1 score.
- Interactive Sidebar: Allows users to select datasets, models, and hyperparameters easily.
- Model Documentation: Provides links to detailed documentation for each model to help users understand their functionalities and applications.
- Comprehensive Visualizations: Features a range of plots and charts to visualize model performance and tuning effects.
Explore the live application here: ML Model Visualizer