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load_diabetes — scikit-learn 1.7.2 documentation
load_diabetes () >>> diabetes . target [: 3 ] array([151., 75., 141.]) >>>...diabetes . data . shape (442, 10) Gallery examples # Model Complexity...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html -
config_context — scikit-learn 1.7.2 documentation
print ‘SVC()’, but would print ‘SVC(C=1.0, cache_size=200, …)’...float ( 'nan' )]) Traceback (most recent call last): ... ValueError...scikit-learn.org/stable/modules/generated/sklearn.config_context.html -
make_s_curve — scikit-learn 1.7.2 documentation
X . shape (100, 3) >>> t . shape (100,) Gallery examples # Comparison...sklearn.datasets. make_s_curve ( n_samples = 100 , * , noise = 0.0 , random_state...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_s_curve.html -
MetaEstimatorMixin — scikit-learn 1.7.2 documen...
, X , y = None ): ... if self . estimator is None : ... self...self . estimator_ = LogisticRegression () ... else : ... self . estimator_...scikit-learn.org/stable/modules/generated/sklearn.base.MetaEstimatorMixin.html -
mutual_info_score — scikit-learn 1.7.2 document...
= [ 0 , 1 , 1 , 0 , 1 , 0 ] >>> labels_pred = [ 0 , 1 , 0 , 0...switching \(U\) (i.e label_true ) with \(V\) (i.e. label_pred ) will...scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html -
normalized_mutual_info_score — scikit-learn 1.7...
fo_score ([ 0 , 0 , 1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> norm...fo_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 If classes...scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html -
ConfusionMatrixDisplay — scikit-learn 1.7.2 doc...
classes_ ) >>> disp . plot () <...> >>> plt . show () classmethod...Sample weights. normalize {‘true’, ‘pred’, ‘all’}, default=None...scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html -
RocCurveDisplay — scikit-learn 1.7.2 documentation
0.1 , 0.4 , 0.35 , 0.8 ]) >>> fpr , tpr , thresholds = metrics...np . array ([ 0 , 0 , 1 , 1 ]) >>> y_score = np . array ([ 0.1...scikit-learn.org/stable/modules/generated/sklearn.metrics.RocCurveDisplay.html -
cross_val_score — scikit-learn 1.7.2 documentation
instead. E.g.: cross_val_score(..., params={'groups': groups})...print ( cross_val_score ( lasso , X , y , cv = 3 )) [0.3315057...scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html -
StratifiedKFold — scikit-learn 1.7.2 documentation
np . array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]]) >>>...enumerate ( skf . split ( X , y )): ... print ( f "Fold { i } :" ) ......scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html