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:
