Ensemble techniques

Ensemble techniques are powerful methods used to improve the performance of machine learning models, particularly in scenarios with imbalanced datasets, rare events, and edge cases. These techniques combine multiple base models to create a more robust and accurate final prediction. Let’s discuss some popular ensemble techniques.

Bagging

Bootstrap aggregating (bagging) is an ensemble technique that creates multiple bootstrap samples (random subsets with replacement) from the training data and trains a separate base model on each sample. The final prediction is obtained by averaging or voting the predictions of all base models. Bagging is particularly useful when dealing with high variance and complex models, as it reduces overfitting and enhances the model’s generalization ability. Here are the key concepts associated with bagging:

• Bootstrap sampling: The bagging process begins by creating multiple random subsets of the training data through...