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.

Advantages:

– 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.

Disadvantages:

– 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.