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Differences between Bagging and Boosting

Bagging (Bootstrap Aggregating) and Boosting are both ensemble learning techniques that aim to improve the predictive performance of machine learning models by combining multiple base learners. However, they differ in their approach to training and how they leverage the base learners’ predictions to improve model performance. Bagging focuses on reducing variance, whereas Boosting focuses on…

CEO 116 views

BaggingClassifier from Scikit-Learn

The BaggingClassifier is an ensemble meta-estimator in machine learning, belonging to the bagging family of methods. Bagging stands for Bootstrap Aggregating. The main idea behind bagging is to reduce variance by averaging the predictions of multiple base estimators trained on different subsets of the training data. Here’s how the BaggingClassifier works: The BaggingClassifier in scikit-learn…