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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.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: AdaBoost Ensembles for Classification.

### What should I learn from this recipe?

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- AdaBoost Ensembles for Classification.

AdaBoost Ensembles for Classification:

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

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