All glossary terms
modelsintermediate
Random Forest
An ensemble learning method that builds many decision trees and combines their predictions.
In more detail
Random Forests use bagging and feature randomness to build diverse trees, then average predictions. Reduces overfitting, handles non-linear relationships, and provides feature importance. Robust for classification and regression.
Examples
- Classification with categorical and numeric features
- Regression with complex non-linear relationships
- Feature importance analysis
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