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SVM with custom kernel — scikit-learn 1.8...
T (0 1) """ M = np . array...array ([[ 2 , 0 ], [ 0 , 1.0 ]]) return np . dot ( np . dot (...scikit-learn.org/stable/auto_examples/svm/plot_custom_kernel.html -
Lasso, Lasso-LARS, and Elastic Net paths —...
legend (( l1 [ - 1 ], l2 [ - 1 ]), ( "Lasso"...plt . legend (( l1 [ - 1 ], l2 [ - 1 ]), ( "Lasso"...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.html -
Feature importances with a forest of trees R...
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead...[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html -
Comparing Target Encoder with Other Encoders &#...
versionchanged:: 1.1 Added option 'quantile'. .. versionchanged:: 1.3 Added...versionchanged:: 1.1 Added option 'quantile'. .. versionchanged:: 1.3 Added...scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder.html -
SGD: Maximum margin separating hyperplane ̵...
linspace ( - 1 , 5 , 10 ) yy = np . linspace ( - 1 , 5 , 10 ) X1...= p [ 0 ] levels = [ - 1.0 , 0.0 , 1.0 ] linestyles = [ "dashed"...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html -
Gradient Boosting Out-of-Bag estimates — ...
subsample < 1.0 ), the estimates are derived...y = random_state . binomial ( 1 , p , size = n_samples ) X = np...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html -
Demo of HDBSCAN clustering algorithm — sc...
centers = [[ 1 , 1 ], [ - 1 , - 1 ], [ 1.5 , - 1.5 ]] X , labels_true...== - 1 : # Black used for noise. col = [ 0 , 0 , 0 , 1 ] class_index...scikit-learn.org/stable/auto_examples/cluster/plot_hdbscan.html -
7.9. Transforming the prediction target (y) ...
1, 1, 1], [0, 0, 1, 0, 0], [1, 1, 0, 1, 0], [1, 1, 1, 1,...1, 1], [1, 1, 1, 0, 0]]) For more information about multilabel...scikit-learn.org/stable/modules/preprocessing_targets.html -
Multiclass Receiver Operating Characteristic (R...
1 ], [ 0 , 1 ], "k--" , label...xlim = ( - 0.01 , 1.01 ), ylim = ( - 0.01 , 1.01 ), ) We confirm...scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html -
Ridge coefficients as a function of the L2 Regu...
random_state = 1 ) # Obtain the true coefficients...fig , axs = plt . subplots ( 1 , 2 , figsize = ( 20 , 6 )) coefs...scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_coeffs.html