AdaBoost - Adaptive Boosting

Published on: April 25, 2021

AdaBoost - Adaptive Boosting

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AdaBoost, short for Adaptive Boosting, of Freund and Schapire, was the first practical boosting algorithm and remains one of the most widely used and studied ones even today. Boosting is a general strategy for learning "strong models" by combining multiple simpler ones (weak models or weak learners).

A "weak learner" is a model that will do at least slightly better than chance. AdaBoost can be applied to any classification algorithm, but most often, it's used with Decision Stumps - Decision Trees with only one node and two leaves.

Decision Stump

Decision Stumps alone are not an excellent way to make predictions. A full-grown decision tree combines the decisions from all features to predict the target value. A stump, on the other hand, can only use one feature to make predictions.

How does the AdaBoost algorithm work?

  1. Initialize sample weights uniformly as .
  2. For each iteration :

Step 1: A weak learner (e.g. a decision stump) is trained on top of the weighted training data . The weight of each sample indicates how important it is to classify the sample correctly.

Step 2: After training, the weak learner gets a weight based on its accuracy


Step 3: The weights of misclassified samples are updated

Step 4: Renormalize weights so they sum up to 1

  1. Make predicts using a linear combination of the weak learners
Adaboost Training



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