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1.5. Stochastic Gradient Descent — scikit-learn...
[ 1. , 1. ]] >>> y = [ 0 , 1 ] >>> clf = SGDClassifier...\Vert^2 + b\nu + \frac{1}{n} \sum_{i=1}^n \max(0, 1 - (\langle w, x_i...scikit-learn.org/stable/modules/sgd.html -
v_measure_score — scikit-learn 1.8.0 documentation
1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> v_measure_score...v_measure_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Labelings that...scikit-learn.org/stable/modules/generated/sklearn.metrics.v_measure_score.html -
matthews_corrcoef — scikit-learn 1.8.0 document...
[ + 1 , + 1 , + 1 , - 1 ] >>> y_pred = [ + 1 , - 1 , + 1 , +...value between -1 and +1. A coefficient of +1 represents a perfect...scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html -
label_binarize — scikit-learn 1.8.0 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 -
rand_score — scikit-learn 1.8.0 documentation
1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Labelings that...rand_score ([ 0 , 0 , 1 , 2 ], [ 0 , 0 , 1 , 1 ]) 0.83 Gallery examples...scikit-learn.org/stable/modules/generated/sklearn.metrics.rand_score.html -
classification_report — scikit-learn 1.8.0 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 -
OneClassSVM — scikit-learn 1.8.0 documentation
predict ( X ) array([-1, 1, 1, 1, -1]) >>> clf . score_samples...default=-1 Hard limit on iterations within solver, or -1 for no...scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html -
scale — scikit-learn 1.8.0 documentation
independently array([[-1., 1., 1.], [ 1., -1., -1.]]) >>> scale ( X...scale >>> X = [[ - 2 , 1 , 2 ], [ - 1 , 0 , 1 ]] >>> scale ( X ,...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html -
mean_shift — scikit-learn 1.8.0 documentation
array ([[ 1 , 1 ], [ 2 , 1 ], [ 1 , 0 ], ... [ 4 , 7...array([[3.33, 6. ], [1.33, 0.66]]) >>> labels array([1, 1, 1, 0, 0, 0])...scikit-learn.org/stable/modules/generated/sklearn.cluster.mean_shift.html -
roc_curve — scikit-learn 1.8.0 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