sklearn.model_selection.ShuffleSplit — scikit-learn 0.20.0 documentation
ShuffleSplit# class sklearn.model_selection.ShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None)[source]# Random permutation cross-validator. Yields indices to split data into training and test sets. Note: contrary to other cross-validation strategies, random splits do not guarantee that test sets across all folds will be mutually exclusive, and might include overlappin
sklearn.model_selection.train_test_split — scikit-learn 0.19.2 documentation
train_test_split# sklearn.model_selection.train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)[source]# Split arrays or matrices into random train and test subsets. Quick utility that wraps input validation, next(ShuffleSplit().split(X, y)), and application to input data into a single call for splitting (and optionally subsampling) data into a
cross_val_score
cross_val_score# sklearn.model_selection.cross_val_score(estimator, X, y=None, *, groups=None, scoring=None, cv=None, n_jobs=None, verbose=0, params=None, pre_dispatch='2*n_jobs', error_score=nan)[source]# Evaluate a score by cross-validation. Read more in the User Guide. Parameters: estimatorestimator object implementing ‘fit’The object to use to fit the data. X{array-like, sparse matrix} of shap
learning_curve
learning_curve# sklearn.model_selection.learning_curve(estimator, X, y, *, groups=None, train_sizes=array([0.1, 0.33, 0.55, 0.78, 1.]), cv=None, scoring=None, exploit_incremental_learning=False, n_jobs=None, pre_dispatch='all', verbose=0, shuffle=False, random_state=None, error_score=nan, return_times=False, fit_params=None, params=None)[source]# Learning curve. Determines cross-validated training
StratifiedShuffleSplit
StratifiedShuffleSplit# class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None)[source]# Stratified ShuffleSplit cross-validator. Provides train/test indices to split data in train/test sets. This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified randomized folds. The folds are made by p
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