Python Machine Learning by Sebastian Raschka

Author:Sebastian Raschka
Language: eng
Format: epub, pdf
Publisher: Packt Publishing

After the grid search has completed, we can print the different hyperparameter value combinations and the average ROC AUC scores computed via 10-fold cross-validation. The code is as follows:

>>> for params, mean_score, scores in grid.grid_scores_: ... print("%0.3f+/-%0.2f %r" ... % (mean_score, scores.std() / 2, params)) 0.967+/-0.05 {'pipeline-1__clf__C': 0.001, 'decisiontreeclassifier__max_depth': 1} 0.967+/-0.05 {'pipeline-1__clf__C': 0.1, 'decisiontreeclassifier__max_depth': 1} 1.000+/-0.00 {'pipeline-1__clf__C': 100.0, 'decisiontreeclassifier__max_depth': 1} 0.967+/-0.05 {'pipeline-1__clf__C': 0.001, 'decisiontreeclassifier__max_depth': 2} 0.967+/-0.05 {'pipeline-1__clf__C': 0.1, 'decisiontreeclassifier__max_depth': 2} 1.000+/-0.00 {'pipeline-1__clf__C': 100.0, 'decisiontreeclassifier__max_depth': 2} >>> print('Best parameters: %s' % grid.best_params_) Best parameters: {'pipeline-1__clf__C': 100.0, 'decisiontreeclassifier__max_depth': 1} >>> print('Accuracy: %.2f' % grid.best_score_) Accuracy: 1.00

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