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GaussianProcessClassifier — scikit-learn 1.8.0 ...
1, 3.2, and 5.1 from [RW2006] . Internally,...None is passed, the kernel “1.0 * RBF(1.0)” is used as default. Note...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html -
Examples of Using FrozenEstimator — scikit-lear...
same three data points: [1 1 1] Now imagine you’d want to set...scikit-learn 1.5 Release Highlights for scikit-learn 1.5 Probability...scikit-learn.org/stable/auto_examples/frozen/plot_frozen_examples.html -
Plot the support vectors in LinearSVC — scikit-...
subplot ( 1 , 2 , i + 1 ) plt . scatter ( X [:, 0 ], X [:, 1 ], c...colors = "k" , levels = [ - 1 , 0 , 1 ], alpha = 0.5 , linestyles...scikit-learn.org/stable/auto_examples/svm/plot_linearsvc_support_vectors.html -
Orthogonal Matching Pursuit — scikit-learn 1.8....
1 , 1 ) plt . xlim ( 0 , 512 ) plt...make_sparse_coded_signal ( n_samples = 1 , n_components = n_components...scikit-learn.org/stable/auto_examples/linear_model/plot_omp.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 -
Spectral clustering for image segmentation — sc...
[ 0 ]) ** 2 + ( y - center1 [ 1 ]) ** 2 < radius1 ** 2 circle2...[ 0 ]) ** 2 + ( y - center2 [ 1 ]) ** 2 < radius2 ** 2 circle3...scikit-learn.org/stable/auto_examples/cluster/plot_segmentation_toy.html -
load_breast_cancer — scikit-learn 1.8.0 documen...
1, 0]) >>> list ( data . target_names...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.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 -
load_iris — scikit-learn 1.8.0 documentation
scikit-learn 1.2 Release Highlights for scikit-learn 1.2 Decision...target [ samples ] array([0, 0, 1]) >>> data . target_names [ data...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html -
plot_tree — scikit-learn 1.8.0 documentation
“x[1]”, …). class_names array-like...scikit-learn.org/stable/modules/generated/sklearn.tree.plot_tree.html