Random Forest is like having a team of decision trees rather than relying on just one. Each tree makes a prediction, and the forest combines them to give a final result.
This ensemble approach makes Random Forest more robust and accurate than a single decision tree.
How Random Forest Works
- Bootstrap Sampling: Randomly select subsets of the data to train multiple trees.
- Feature Randomness: Each tree considers a random subset of features when splitting nodes.
- Tree Building: Grow each decision tree independently.
- Aggregation: For classification, use majority voting; for regression, take the average prediction.
This randomness helps reduce overfitting while maintaining accuracy.
Advantages of Random Forest
- Handles large datasets and high-dimensional features
- Less prone to overfitting compared to a single tree
- Can handle both classification and regression tasks
- Provides feature importance insights
Disadvantages
- Can be slower to train than a single tree
- Harder to interpret than a single decision tree
- Requires more memory for large forests
Real-World Examples
- Fraud detection in banking
- Predicting customer churn in telecom
- Medical diagnosis and disease prediction
- Stock market prediction
- Recommendation systems
Conclusion
Random Forest combines the wisdom of multiple decision trees to create a powerful, reliable, and versatile model. It’s a go-to choice for many real-world ML tasks.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/