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ClassifierChain — scikit-learn 1.8.0 documentation
X_test ) array([[1., 1., 0.], [1., 0., 0.], [0., 1., 0.]]) >>> chain...array([[0.8387, 0.9431, 0.4576], [0.8878, 0.3684, 0.2640], [0.0321,...scikit-learn.org/stable/modules/generated/sklearn.multioutput.ClassifierChain.html -
LeavePOut — scikit-learn 1.8.0 documentation
) Fold 0: Train: index=[2 3] Test: index=[0 1] Fold 1: Train:...index=[0 2] Test: index=[1 3] Fold 5: Train: index=[0 1] Test:...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePOut.html -
cross_val_predict — scikit-learn 1.8.0 document...
means 1 unless in a joblib.parallel_backend context. -1 means...None , n_jobs = None , verbose = 0 , params = None , pre_dispatch...scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html -
median_absolute_error — scikit-learn 1.8.0 docu...
y_pred ) 0.5 >>> y_true = [[ 0.5 , 1 ], [ - 1 , 1 ], [ 7 , -...y_true = [ 3 , - 0.5 , 2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.median_absolute_error.html -
cosine_similarity — scikit-learn 1.8.0 document...
X = [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1...1 , 1 , 0 ]] >>> cosine_similarity ( X , Y ) array([[0. , 0....scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html -
ConfusionMatrixDisplay — scikit-learn 1.8.0 doc...
display labels are set from 0 to n_classes - 1 . Attributes : im_ matplotlib...random_state = 0 ) >>> clf = SVC ( random_state = 0 ) >>> clf ....scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html -
learning_curve — scikit-learn 1.8.0 documentation
train_sizes = array([0.1, 0.33, 0.55, 0.78, 1.]) , cv = None , scoring...(n_ticks,), default=np.linspace(0.1, 1.0, 5) Relative or absolute numbers...scikit-learn.org/stable/modules/generated/sklearn.model_selection.learning_curve.html -
LeaveOneGroupOut — scikit-learn 1.8.0 documenta...
group=[1 1] Fold 1: Train: index=[0 1], group=[1 1] Test: index=[2...array ([ 1 , 2 , 1 , 2 ]) >>> groups = np . array ([ 1 , 1 , 2 ,...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneGroupOut.html -
RepeatedKFold — scikit-learn 1.8.0 documentation
array ([ 0 , 0 , 1 , 1 ]) >>> rkf = RepeatedKFold...... Fold 0: Train: index=[0 1] Test: index=[2 3] Fold 1: Train:...scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedKFold.html -
paired_cosine_distances — scikit-learn 1.8.0 do...
X = [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1...1 , 1 , 0 ]] >>> paired_cosine_distances ( X , Y ) array([0.5...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.paired_cosine_distances.html