Hướng dẫn dùng sklearn cross_validate python
Evaluate metric(s) by cross-validation and also record fit/score times. Read more in the User Guide. Parameters:estimatorestimator object implementing ‘fit’The object to use to fit the data. Xarray-like of shape (n_samples, n_features)The data to fit. Can be for example a list, or an array. yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=NoneThe target variable to try to predict in the case of supervised learning. groupsarray-like of shape (n_samples,), default=NoneGroup labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group”
cv instance (e.g., Strategy to evaluate the performance of the cross-validated model on the test set. If
If
See Specifying multiple metrics for evaluation for an example. cvint, cross-validation generator or an iterable, default=NoneDetermines the cross-validation splitting strategy. Possible inputs for cv are:
For int/None inputs, if the estimator is a classifier and Refer User Guide for the various cross-validation strategies that can be used here. Changed in version 0.22: Number of jobs to run in
parallel. Training the estimator and computing the score are parallelized over the cross-validation splits. The verbosity level. fit_paramsdict, default=NoneParameters to pass to the fit method of the estimator. Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: return_train_scorebool, default=False Whether to include train scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance. New in version 0.19. Changed in version 0.21: Default value was changed from Whether to return the estimators fitted on each split. New in version 0.20. error_score‘raise’ or numeric, default=np.nanValue to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. New in version 0.20. Returns:scoresdict of float arrays of shape (n_splits,)Array of scores of the estimator for each run of the cross validation. A dict of arrays containing the score/time arrays for each scorer is returned. The possible keys for this
Examples >>> from sklearn import datasets, linear_model >>> from sklearn.model_selection import cross_validate >>> from sklearn.metrics import make_scorer >>> from sklearn.metrics import confusion_matrix >>> from sklearn.svm import LinearSVC >>> diabetes = datasets.load_diabetes() >>> X = diabetes.data[:150] >>> y = diabetes.target[:150] >>> lasso = linear_model.Lasso() Single metric evaluation using >>> cv_results = cross_validate(lasso, X, y, cv=3) >>> sorted(cv_results.keys()) ['fit_time', 'score_time', 'test_score'] >>> cv_results['test_score'] array([0.3315057 , 0.08022103, 0.03531816]) Multiple
metric evaluation using >>> scores = cross_validate(lasso, X, y, cv=3, ... scoring=('r2', 'neg_mean_squared_error'), ... return_train_score=True) >>> print(scores['test_neg_mean_squared_error']) [-3635.5... -3573.3... -6114.7...] >>> print(scores['train_r2']) [0.28009951 0.3908844 0.22784907] Examples using |