AdaBoost Ensembles for Classification | Jupyter Notebook | Python Data Science for beginners

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AdaBoost, short for Adaptive Boosting, is a powerful ensemble method for classification in python. It is a meta-algorithm that combines multiple weak classifiers to form a strong one. The basic idea behind AdaBoost is to fit a sequence of weak learners (i.e., models that are only slightly better than random guessing) on repeatedly modified versions of the data. The predictions from all of them are then combined through a weighted majority vote (or sum) to produce the final prediction.

The data modifications at each so-called boosting iteration consist of applying weights to each of the training samples. Initially, those weights are all set to 1/N, so that the first step simply trains a weak learner on the original data. For each successive iteration, the sample weights are individually modified and the learning algorithm is reapplied to the reweighted data. At a given step, those training examples that were incorrectly predicted by the boosted model induced at the previous step have their weights increased, whereas the weights are decreased for those that were predicted correctly. As iterations proceed, examples that are difficult to predict receive ever-increasing influence. Each subsequent weak learner is thereby forced to concentrate on the examples that are missed by the previous ones in the sequence.

AdaBoost can be used with any learning algorithm, but it is best used with decision trees. The resulting classifier is a linear combination of weak classifiers, where the coefficients are learned by the AdaBoost algorithm. AdaBoost can also be used for both binary and multi-class classification problems.

In summary, AdaBoost is a powerful ensemble method for classification that combines multiple weak classifiers to form a strong one. It works by repeatedly modifying the data and applying a weak learning algorithm to the reweighted data, with the goal of focusing on examples that are difficult to predict. AdaBoost is best used with decision trees and can be applied to both binary and multi-class classification problems.


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