Random Forest Overview
Random forests are ensemble methods using multiple decision trees. They improve accuracy and reduce overfitting.
Implementation
RandomForestClassifier from sklearn.ensemble. n_estimators sets number of trees. max_depth limits tree complexity.
Parameters
n_estimators: more trees improve performance but increase computation. max_features: number of features considered at each split.
min_samples_split, min_samples_leaf control tree complexity. These prevent overfitting.
Feature Importance
Feature importance measures each feature's contribution. Built-in: rf.feature_importances_. Higher values indicate more important features.
This helps interpret models and select features.
Key Takeaways
- Random forests are robust ensemble methods
- They provide built-in feature importance
- Key parameters: n_estimators, max_depth, max_features