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PolynomialCountSketch — scikit-learn 1.7.2 docu...
1 , 1 ], [ 1 , 0 ], [ 0 , 1 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>>...SGDClassifier(max_iter=10) >>> clf . score ( X_features , y ) 1.0 For...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.html -
LogisticRegressionCV — scikit-learn 1.7.2 docum...
‘l2’ yes ‘newton-cholesky’ ‘l2’, yes ‘sag’ ‘l2’, yes ‘saga’ ‘elasticnet’,...multiclass ‘lbfgs’ ‘l2’ yes ‘liblinear’ ‘l1’, ‘l2’ no ‘newton-cg’ ‘l2’...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html -
ElasticNet — scikit-learn 1.7.2 documentation
linear_model. ElasticNet ( alpha = 1.0 , * , l1_ratio = 0.5 , fit_intercept...n_samples ) * || y - Xw ||^ 2_2 + alpha * l1_ratio * || w || _1 + 0.5...scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html -
OrthogonalMatchingPursuit — scikit-learn 1.7.2 ...
12. (December 1993), pp. 3397-3415. ( https://www.di.ens.fr/~m...OrthogonalMatchingPu () . fit ( X , y ) >>> reg . score ( X , y ) 0.9991 >>>...scikit-learn.org/stable/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuit.html -
f_classif — scikit-learn 1.7.2 documentation
array([2.21e+02, 7.02e-01, 1.70e+00, 9.31e-01, 5.41e+00, 3.25e-01,...array([7.14e-27, 4.04e-01, 1.96e-01, 3.37e-01, 2.21e-02, 5.70e-01,...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_classif.html -
lars_path — scikit-learn 1.7.2 documentation
random_state = 0 ... ) >>> true_coef array([ 0. , 0. , 0. , 97.9, 45.7])...method=’lasso’ is: ( 1 / ( 2 * n_samples )) * || y - Xw ||^ 2_2 + alpha *...scikit-learn.org/stable/modules/generated/sklearn.linear_model.lars_path.html -
brier_score_loss — scikit-learn 1.7.2 documenta...
y_prob = np . array ([ 0.1 , 0.9 , 0.8 , 0.3 ]) >>> brier_score_loss...y_true_categorical = np . array ([ "spam" , "ham" , "ham" , "spam" ]) >>> y_prob...scikit-learn.org/stable/modules/generated/sklearn.metrics.brier_score_loss.html -
root_mean_squared_error — scikit-learn 1.7.2 do...
- 1 , 1 ],[ 7 , - 6 ]] >>> y_pred = [[ 0 , 2 ],[ - 1 , 2 ],[...= [ 3 , - 0.5 , 2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.root_mean_squared_error.html -
NearestNeighbors — scikit-learn 1.7.2 documenta...
neigh . radius_neighbors ( ... [[ 0 , 0 , 1.3 ]], 0.4 , return_distance...queries. algorithm {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html -
pairwise_distances_argmin — scikit-learn 1.7.2 ...
= [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 ,...‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘minkowski’,...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances_argmin.html