classification_report# sklearn.metrics.classification_report(y_true, y_pred, *, labels=None, target_names=None, sample_weight=None, digits=2, output_dict=False, zero_division='warn')[source]# Build a text report showing the main classification metrics. Read more in the User Guide. Parameters: y_true1d array-like, or label indicator array / sparse matrixGround truth (correct) target values. y_pred1

class sklearn.grid_search.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch=‘2*n_jobs’, error_score=’raise’)[source]¶ Exhaustive search over specified parameter values for an estimator. Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.GridSearchCV instead. Important memb

This documentation is for scikit-learn version 0.17.1 — Other versions If you use the software, please consider citing scikit-learn . Parameter estimation using grid search with cross-validation Parameter estimation using grid search with cross-validation ¶ This examples shows how a classifier is optimized by cross-validation, which is done using the sklearn.grid_search.GridSearchCV object on a de