Hands-On Machine Learning for Algorithmic Trading

AdaBoost with sklearn

As part of its ensemble module, sklearn provides an AdaBoostClassifier implementation that supports two or more classes. The code examples for this section are in the notebook gbm_baseline that compares the performance of various algorithms with a dummy classifier that always predicts the most frequent class.

We need to first define a base_estimator as a template for all ensemble members and then configure the ensemble itself. We'll use the default DecisionTreeClassifier with max_depth=1—that is, a stump with a single split. The complexity of the base_estimator is a key tuning parameter because it depends on the nature of the data. As demonstrated in the previous chapter, changes to max_depth should be combined with ...