What is Random Forest?
Random Forest is an ensemble learning technique that builds multiple decision trees and merges their results to improve accuracy and reduce overfitting. It is widely used in classification and regression tasks.
How Does It Work?
- Randomly selects subsets of training data.
- Creates multiple decision trees on different subsets.
- Averages predictions (for regression) or uses majority voting (for classification).
- Reduces overfitting by leveraging multiple models.
Advantages of Random Forest
- Handles large datasets with higher accuracy.
- Works well for both classification and regression tasks.
- Reduces overfitting compared to a single decision tree.
- Handles missing values and maintains performance.