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make_blobs — scikit-learn 1.6.1 documentation
scikit-learn 1.1 Release Highlights for scikit-learn 1.1 Release Highlights...2) >>> y array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0]) >>> X , y = make_blobs...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html -
silhouette_samples — scikit-learn 1.6.1 documen...
The best value is 1 and the worst value is -1. Values near 0 indicate...2 <= n_labels <= n_samples - 1 . This function returns the Silhouette...scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_samples.html -
SVC — scikit-learn 1.6.1 documentation
array ([[ - 1 , - 1 ], [ - 2 , - 1 ], [ 1 , 1 ], [ 2 , 1 ]]) >>>...default=-1 Hard limit on iterations within solver, or -1 for no...scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html -
KNeighborsTransformer — scikit-learn 1.6.1 docu...
() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]]) set_output...bors=1) >>> print ( neigh . kneighbors ([[ 1. , 1. , 1. ]]))...scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsTransformer.html -
IsotonicRegression — scikit-learn 1.6.1 documen...
1 , .2 ]) array([1.8628..., 3.7256...])...n_samples = 10 , n_features = 1 , random_state = 41 ) >>> iso_reg...scikit-learn.org/stable/modules/generated/sklearn.isotonic.IsotonicRegression.html -
TargetEncoder — scikit-learn 1.6.1 documentation
1 ] * 15 + [ 20.4 ] * 5 + [ 20.1 ] * 25 + [ 21.2...scikit-learn 1.3 Release Highlights for scikit-learn 1.3 Comparing...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html -
Kernel — scikit-learn 1.6.1 documentation
length_scale = 1.0 ): ... self . length_scale =...2.0 ) >>> X = np . array ([[ 1 , 2 ], [ 3 , 4 ]]) >>> print (...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Kernel.html -
label_binarize — scikit-learn 1.6.1 documentation
label_binarize ([ 1 , 6 ], classes = [ 1 , 2 , 4 , 6 ]) array([[1, 0, 0,...label_binarize ([ 1 , 6 ], classes = [ 1 , 6 , 4 , 2 ]) array([[1, 0, 0,...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.label_binarize.html -
classification_report — scikit-learn 1.6.1 docu...
50 1.00 0.67 1 class 1 0.00 0.00 0.00 1 class 2 1.00 0.67...>>> y_pred = [ 1 , 1 , 0 ] >>> y_true = [ 1 , 1 , 1 ] >>> print...scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html -
roc_curve — scikit-learn 1.6.1 documentation
y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise...labels are not either {-1, 1} or {0, 1}, then pos_label should...scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html