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minmax_scale — scikit-learn 1.7.2 documen...
1 , 2 ], [ - 1 , 0 , 1 ]] >>> minmax_scale...independently array([[0., 1., 1.], [1., 0., 0.]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.minmax_scale.html -
confusion_matrix — scikit-learn 1.7.2 doc...
1 , 0 , 1 ], [ 1 , 1 , 1 , 0 ]) . ravel ()...) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]]) >>> y_true...scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html -
OPTICS — scikit-learn 1.7.2 documentation
1, 1, 1]) For a more detailed example...min_samples int > 1 or float between 0 and 1, default=5 The number...scikit-learn.org/stable/modules/generated/sklearn.cluster.OPTICS.html -
make_sparse_spd_matrix — scikit-learn 1.7...
array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0.,...elements all 1. smallest_coef float, default=0.1 The value of...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_spd_matrix.html -
DecisionTreeClassifier — scikit-learn 1.7...
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead...instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The “balanced” mode uses...scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html -
SGDClassifier — scikit-learn 1.7.2 docume...
array ([[ - 1 , - 1 ], [ - 2 , - 1 ], [ 1 , 1 ], [ 2 , 1 ]]) >>>...(clip(decision_function(X), -1, 1) + 1) / 2. For other loss functions...scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html -
average_precision_score — scikit-learn 1....
1 , 1 ]) >>> y_scores = np . array ([ 0.1 , 0.4..., 0.1 ], ... [ 0.4 , 0.3 , 0.3 ], ... [ 0.1 , 0.8 , 0.1 ], ......scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html -
pairwise_distances_argmin — scikit-learn ...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0...deprecated from SciPy 1.9 and will be removed in SciPy 1.11. Note 'matching'...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances_argmin.html -
KBinsDiscretizer — scikit-learn 1.7.2 doc...
[ 1., 1., 1., 0.], [ 2., 2., 2., 1.], [ 2., 2., 2.,...>>> X = [[ - 2 , 1 , - 4 , - 1 ], ... [ - 1 , 2 , - 3 , - 0.5...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html -
NuSVR — scikit-learn 1.7.2 documentation
Added in version 1.1. n_support_ ndarray of shape (1,), dtype=int32...(0, 1]. By default 0.5 will be taken. C float, default=1.0 Penalty...scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVR.html