Hands-On Machine Learning for Algorithmic Trading

GridsearchCV for decision trees

sklearn provides a method to define ranges of values for multiple hyperparameters. It automates the process of cross-validating the various combinations of these parameter values to identify the optimal configuration. Let's walk through the process of automatically tuning your model.

The first step is to instantiate a model object and define a dictionary where the keywords name the hyperparameters, and the values list the parameter settings to be tested:

clf = DecisionTreeClassifier(random_state=42)param_grid = {'max_depth': range(10, 20),              'min_samples_leaf': [250, 500, 750],              'max_features': ['sqrt', 'auto']              }

Then, instantiate the GridSearchCV object, providing the estimator object and parameter grid, ...