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VarianceThreshold — scikit-learn 1.7.2 document...
= [[ 0 , 2 , 0 , 3 ], [ 0 , 1 , 4 , 3 ], [ 0 , 1 , 1 , 3 ]] >>>...generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"] . If input_features...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html -
sklearn.frozen — scikit-learn 1.7.2 documentation
top Ctrl + K GitHub Choose version sklearn.frozen # FrozenEstimator...fitted estimator to prevent re-fitting....scikit-learn.org/stable/api/sklearn.frozen.html -
SelectFdr — scikit-learn 1.7.2 documentation
generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"] . If input_features..., alpha = 0.01 ) . fit_transform ( X , y ) >>> X_new . shape...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFdr.html -
orthogonal_mp — scikit-learn 1.7.2 documentation
12. (December 1993), pp. 3397-3415. ( https://www.di.ens.fr/~m...orthogonal_mp ( X , y ) >>> coef . shape (100,) >>> X [: 1 ,] @ coef...scikit-learn.org/stable/modules/generated/sklearn.linear_model.orthogonal_mp.html -
Probability Calibration curves — scikit-learn 1...
0 ), ( 2 , 1 ), ( 3 , 0 ), ( 3 , 1 )] for i , ( _ , name ) in..., "Logistic" ), ( gnb , "Naive Bayes" ), ( gnb_isotonic , "Naive...scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html -
Prediction Intervals for Gradient Boosting Regr...
atleast_2d ( rng . uniform ( 0 , 10.0 , size = 1000 )) . T expected_y...alpha=0.05, 0.5, 0.95. The models obtained for alpha=0.05 and alpha=0.95...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html -
Robust covariance estimation and Mahalanobis di...
06695631e-03 1.22747343e+00]] MLE: [[ 3.23773583 -0.24640578] [-0.24640578...\[d_{(\mu,\Sigma)}(x_i)^2 = (x_i - \mu)^T\Sigma^{-1}(x_i - \mu)\]...scikit-learn.org/stable/auto_examples/covariance/plot_mahalanobis_distances.html -
Release Highlights for scikit-learn 1.3 — sciki...
np . array ([ 0 , 1 , 6 , np . nan ]) . reshape ( - 1 , 1 ) y...random_state = 0 ) . fit ( X , y ) tree . predict ( X ) array([0, 0, 1, 1])...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_3_0.html -
smacof — scikit-learn 1.7.2 documentation
= np . array ([[ 0 , 1 , 2 ], [ 1 , 0 , 3 ], [ 2 , 3 , 0 ]]) >>>...of 0 indicates “perfect” fit, 0.025 excellent, 0.05 good, 0.1...scikit-learn.org/stable/modules/generated/sklearn.manifold.smacof.html -
Product — scikit-learn 1.7.2 documentation
score ( X , y ) 1.0 >>> kernel 1.41**2 * RBF(length_scale=1) __call__...kernel , ... random_state = 0 ) . fit ( X , y ) >>> gpr . score...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Product.html