What is Random Forest Regression?
Random Forest is an ensemble learning method that builds multiple decision trees and merges their predictions.
For regression tasks, it averages the predictions from all trees to produce a final output.
Key Components:
- Decision Trees: Each tree splits the data based on feature values to create homogeneous subgroups
- Bootstrap Sampling: Each tree is trained on a random subset of the data with replacement
- Feature Randomness: At each split, only a random subset of features is considered
- Ensemble Averaging: Final prediction is the average of all individual tree predictions
Hyperparameters:
- Number of Trees: More trees generally improve performance but increase computation time
- Max Depth: Controls how deep each tree can grow (deeper trees can model more complex patterns but may overfit)
- Min Samples Split: Minimum number of samples required to split a node (helps control overfitting)
- Max Features: Number of features to consider when looking for the best split
Advantages:
- Handles non-linear relationships well
- Robust to outliers and noise
- Provides feature importance measures
- Less prone to overfitting than individual decision trees