1. Implement a Random Forest classifier in models/random_forest.py. 2. Add a corresponding Streamlit page at pages/RandomForest.py. 3. Include visualizations such as feature importance and model performance metrics (accuracy, confusion matrix, etc.). 4. Allow parameter tuning for number of estimators, max depth, and criterion (e.g., Gini, entropy).
Implement a Random Forest classifier in models/random_forest.py.
Add a corresponding Streamlit page at pages/RandomForest.py.
Include visualizations such as feature importance and model performance metrics (accuracy, confusion matrix, etc.).
Allow parameter tuning for number of estimators, max depth, and criterion (e.g., Gini, entropy).