Mastering Machine Learning Algorithms

Example of AdaBoost with Scikit-Learn

Let's continue using the Wine dataset in order to analyze the performance of AdaBoost with different parameters. Scikit-Learn, like almost all algorithms, implements both a classifier AdaBoostClassfier (based on the algorithm SAMME and SAMME.R) and a regressor AdaBoostRegressor (based on the algorithm R2). In this case, we are going to use the classifier, but I invite the reader to test the regressor using a custom dataset or one of the built-in toy datasets. In both classes, the most important parameters are n_estimators and learning_rate (default value set to 1.0). The default underlying weak learner is always a decision tree, but it's possible to employ other models creating a base instance and passing ...