Bagging vs Boosting in Machine Learning

Bagging and Boosting are both ensemble learning techniques used to improve model performance by combining multiple models. The main difference is that:

• Bagging reduces variance by training models independently.
• Boosting reduces bias by training models sequentially, focusing on previous errors.

Understanding Bagging

Bagging (Bootstrap Aggregating) aims to reduce model variance by training multiple models on different random subsets of the dataset. These subsets are created using bootstrapping, where data points are sampled with replacement.

Each model is trained independently, and their predictions are combined using voting for classification or averaging for regression. As models are trained independently, Bagging works well with high-variance models like decision trees.

• Uses random sampling with replacement
• Trains models independently
• Combines predictions using voting or averaging
• Reduces overfitting
• Used in Random Forest, Credit risk prediction and Medical diagnosis systems

Understanding Boosting

Boosting focuses on improving model accuracy by training models sequentially. Each new model pays more attention to the data points that were misclassified by previous models. Over time, the ensemble becomes better at handling difficult cases.

Boosting is effective for reducing bias and works well even with weak learners.

• Trains models sequentially
• Gives more weight to misclassified samples
• Combines models using weighted voting
• Reduces bias
• Used in Fraud detection

Difference Between Bagging and Boosting

Now lets see a tabular difference between Bagging and Boosting:

When to Use Which Technique

• Use Bagging when your model is overfitting and has high variance, especially with decision trees.
• Use Boosting when your model underfits and you want higher accuracy by learning complex patterns.