Definition:
A supervised learning algorithm that combines multiple decision trees to improve prediction accuracy and reduce overfitting.
Key Points:
Ensemble method: Many decision trees vote for the final prediction
Works for classification and regression
Reduces errors compared to a single decision tree
Applications:
Credit scoring
Stock market prediction
Fraud detection
Advantages:
High accuracy
Handles large datasets well
Less prone to overfitting
Limitations:
Complex and less interpretable than a single tree
Slower for real-time predictions
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