Hands-On Machine Learning for Algorithmic Trading

How to use gradient boosting with sklearn

The ensemble module of sklearn contains an implementation of gradient boosting trees for regression and classification, both binary and multiclass. The following GradientBoostingClassifier initialization code illustrates the key tuning parameters that we previously introduced, in addition to those that we are familiar with from looking at standalone decision tree models. The notebook gbm_tuning_with_sklearn contains the code examples for this section.

The available loss functions include the exponential loss that leads to the AdaBoost algorithm and the deviance that corresponds to the logistic regression for probabilistic outputs. The friedman_mse node quality measure is a variation on the mean squared ...