MTH 522 – 11/13/2023

Random Forest Overview:
Random Forest is a powerful ensemble learning technique that combines multiple models to enhance predictions. By aggregating diverse model outputs, it mitigates overfitting and boosts overall performance.
Key Features:
1. Bagging: Utilizes bagging, randomly sampling subsets from the training data to create diverse training sets for each decision tree. This prevents overfitting.
2. Random Feature Selection: Randomly selects a subset of features at each split node, reducing correlation between trees and improving generalization ability.
3. Decision Tree Construction: Constructs decision trees similarly to individual decision trees but with a random subset of features at each node to avoid overfitting.
Prediction Process:**
Classification: Uses a majority vote among individual trees for the final prediction.
Regression: Averages predictions from all trees for the final regression prediction.
– Improved predictive accuracy compared to individual decision trees.
– Robust to noisy data, overfitting, and works well with minimal tuning.
– Handles both classification and regression tasks.
– Can be computationally expensive for a large number of trees.
– Interpretability challenges with a large number of trees.
– May not perform well on highly imbalanced datasets, requiring more memory and storage.

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