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Adaptive Boosting (or AdaBoost) is a sequential ensembling method that can be used for both classification and regression. It can use any base machine learning model, though it is most commonly used with decision trees.

For AdaBoost, the Sequential Fitting Method is accomplished by updating the weight attached to each of the training dataset observations as we proceed from one base model to the next. The Aggregation Method is a weighted sum of those base models where the model weight is dependent on the error of that particular estimator.

The training of an AdaBoost model is the process of determining the training dataset observation weights at each step as well as the final weight for each base model for aggregation.

In the next exercise we will dive into the details of AdaBoost!

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