Bagging and Boosting

Bagging and boosting are both ensemble techniques in machine learning, where multiple models are used together to achieve better performance than any single model alone. Bagging (Bootstrap Aggregating): Improve the stability and accuracy of machine learning algorithms by combining the results of multiple models. Random subsets (or samples) of the…

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Random Forest

Random Forest is a supervised algorithm and an ensemble learning method that constructs a multitude of decision trees during training and outputs the class that is the mode of the classes (classification) or the mean prediction (regression) of the individual trees. It is known for its flexibility and is used…

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