When should I use bagging in machine learning?

When should I use bagging in machine learning?

Bagging, or Bootstrap Aggregating, is useful when you want to improve the stability and accuracy of machine learning algorithms. It's particularly effective with high-variance models like decision trees.

Can you explain how bagging works?

Sure! Bagging works by creating multiple subsets of the training data through random sampling with replacement. Each subset trains a separate model, and the final prediction is made by averaging the predictions (for regression) or voting (for classification).

What are some common algorithms that use bagging?

Common algorithms that utilize bagging include Random Forests and Bagged Decision Trees. These algorithms benefit from reduced overfitting and improved accuracy.

Are there situations where bagging might not be effective?

Yes, bagging may not be effective for low-variance models, as it can lead to unnecessary complexity without significant performance gains.