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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 -
hamming_loss — scikit-learn 1.7.2 documen...
1 ], [ 1 , 1 ]]), np . zeros (( 2 , 2...between 0 and 1, lower being better. References [ 1 ] Grigorios...scikit-learn.org/stable/modules/generated/sklearn.metrics.hamming_loss.html -
johnson_lindenstrauss_min_dim — scikit-le...
by: (1 - eps) ||u - v||^2 < ||p(u) - p(v)||^2 < (1 + eps)...]0, 1[ and p is a projection by a random Gaussian N(0, 1) matrix...scikit-learn.org/stable/modules/generated/sklearn.random_projection.johnson_lindenstrauss_min_dim... -
NearestCentroid — scikit-learn 1.7.2 docu...
([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2...2 , 1 ], [ 3 , 2 ]]) >>> y = np . array ([ 1 , 1 , 1...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestCentroid.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 -
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 -
accuracy_score — scikit-learn 1.7.2 docum...
1 ], [ 1 , 1 ]]), np . ones (( 2 , 2...= [ 0 , 2 , 1 , 3 ] >>> y_true = [ 0 , 1 , 2 , 3 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html -
AffinityPropagation — scikit-learn 1.7.2 ...
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2.... labels_ array([0, 0, 0, 1, 1, 1]) >>> clustering ....scikit-learn.org/stable/modules/generated/sklearn.cluster.AffinityPropagation.html -
PolynomialCountSketch — scikit-learn 1.7....
[ 1 , 1 ], [ 1 , 0 ], [ 0 , 1 ]] >>> y...Array with random entries in {+1, -1}, used to represent the 2-wise...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.html