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Perceptron — scikit-learn 1.8.0 documentation
l1_ratio <= 1 . l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1....means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html -
DictVectorizer — scikit-learn 1.8.0 documentation
[{ 'foo' : 1 , 'bar' : 2 }, { 'foo' : 3 , 'baz' : 1 }] >>> X =...) >>> X array([[2., 0., 1.], [0., 1., 3.]]) >>> v . inverse_transform...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html -
PCA — scikit-learn 1.8.0 documentation
([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2...more details. Added in version 1.1. power_iteration_normalizer {‘auto’,...scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html -
Comparison of Calibration of Classifiers — scik...
1 ) proba_neg_class = 1 - proba_pos_class proba...[( 2 , 0 ), ( 2 , 1 ), ( 3 , 0 ), ( 3 , 1 )] for i , ( _ , name...scikit-learn.org/stable/auto_examples/calibration/plot_compare_calibration.html -
SGD: Weighted samples — scikit-learn 1.8.0 docu...
) + [ 1 , 1 ], np . random . randn ( 10 , 2 )] y = [ 1 ] * 10...10 + [ - 1 ] * 10 sample_weight = 100 * np . abs ( np . random...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html -
make_regression — scikit-learn 1.8.0 documentation
1.523], [-0.2341, -0.2341], [-0.4694, 0.5425], [ 1.579,...scikit-learn 1.4 Release Highlights for scikit-learn 1.4 Release...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html -
RBF SVM parameters — scikit-learn 1.8.0 documen...
1 , 1e2 ] gamma_2d_range = [ 1e-1 , 1 , 1e1 ] classifiers...{'C': np.float64(1.0), 'gamma': np.float64(0.1)} with a score of...scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html -
max_error — scikit-learn 1.8.0 documentation
1 ] >>> y_pred = [ 4 , 2 , 7 , 1 ] >>> max_error...max_error ( y_true , y_pred ) 1.0 On this page This Page...scikit-learn.org/stable/modules/generated/sklearn.metrics.max_error.html -
Two-class AdaBoost — scikit-learn 1.8.0 documen...
random_state = 1 ) X2 , y2 = make_gaussian_quantiles...es ( mean = ( 3 , 3 ), cov = 1.5 , n_samples = 300 , n_features...scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_twoclass.html -
check_is_fitted — scikit-learn 1.8.0 documentation
fit ([[ 1 , 2 ], [ 1 , 3 ]], [ 1 , 0 ]) LogisticRegression()...scikit-learn.org/stable/modules/generated/sklearn.utils.validation.check_is_fitted.html