Hands-On Machine Learning for Algorithmic Trading

Parameter impact on test scores

The GridSearchCV result stores the average cross-validation scores so that we can analyze how different hyperparameter settings affect the outcome.

The six seaborn swarm plots in the left-hand panel of the below chart show the distribution of AUC test scores for all parameter values. In this case, the highest AUC test scores required a low learning_rate and a large value for max_features. Some parameter settings, such as a low learning_rate, produce a wide range of outcomes that depend on the complementary settings of other parameters. Other parameters are compatible with high scores for all settings use in the experiment:

We will now explore how hyperparameter settings jointly affect the mean cross-validation ...