Random Forest Algorithm

Overview

Random Forest is a powerful machine learning algorithm that creates a "forest" of decision trees and combines their predictions to make more accurate and stable predictions.

How It Works

1. Bootstrap Sampling: Creates multiple random subsets of your training data
2. Tree Building: Builds a decision tree for each subset, using random feature selection
3. Prediction Aggregation:
   • Classification: Takes a majority vote from all trees
   • Regression: Averages predictions from all trees

Advantages

• Robust to Overfitting: Multiple trees reduce risk of memorizing training data
• Handles Missing Data: Can maintain accuracy with missing values
• Feature Importance: Calculates which features are most predictive
• Works with Mixed Data: Handles numerical and categorical features
• No Scaling Required: Unlike some algorithms, doesn't require feature scaling

When to Use Random Forest

• You have a mix of numerical and categorical features
• You want to understand feature importance
• You need a reliable baseline model
• Your dataset has complex, non-linear relationships

Hyperparameters in CMMI-DCC

• Number of Trees: More trees = more stable predictions (default: 100)
• Max Depth: Maximum depth of each tree (prevents overfitting)
• Min Samples Split: Minimum samples required to split a node
• Max Features: Number of features to consider for each split

Related Algorithms

• XGBoost: Another ensemble method with gradient boosting