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Sum — scikit-learn 1.7.1 documentation
) >>> gpr . score ( X , y ) 1.0 >>> kernel 1.41**2 + RBF(length_scale=1)...GaussianProcessRegre ( kernel = kernel , ... random_state = 0 ) . fit ( X , y )...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Sum.html -
Kernel — scikit-learn 1.7.1 documentation
diag ( self , X ): ... return np . ones ( X . shape [ 0 ]) ... def...self , X , Y = None ): ... if Y is None : ... Y = X ... return...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Kernel.html -
graphical_lasso — scikit-learn 1.7.1 documentation
212, -0.209], [ 0.212, 0.221, -0.0817], [-0.209, -0.0817, 0.232]])...covariance. Range is (0, inf]. mode {‘cd’, ‘lars’}, default=’cd’ The...scikit-learn.org/stable/modules/generated/sklearn.covariance.graphical_lasso.html -
make_friedman2 — scikit-learn 1.7.1 documentation
( X [:, 1 ] * X [:, 3 ])) ** 2 ) ** 0.5 + noise * N ( 0 , 1 )...formula: y ( X ) = ( X [:, 0 ] ** 2 + ( X [:, 1 ] * X [:, 2 ] - 1 / (...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman2.html -
Restricted Boltzmann Machine features for digit...
0 , 0 ], [ 0 , 0 , 0 ]], [[ 0 , 0 , 0 ], [ 1 , 0 , 0 ], [ 0 ,..., 0 , 0 ]], [[ 0 , 0 , 0 ], [ 0 , 0 , 1 ], [ 0 , 0 , 0 ]], [[...scikit-learn.org/stable/auto_examples/neural_networks/plot_rbm_logistic_classification.html -
Balance model complexity and cross-validated sc...
score. [1] Hastie, T., Tibshirani, R.,, Friedman, J. (2001). Model...`mean_test_scores`. Parameters ---------- cv_results : dict of numpy(masked)...scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_refit_callable.html -
Evaluation of outlier detection estimators — sc...
"normal." ) . astype ( np . int32 ) X , _ , y , _ = train_test_split...= { "LOF" : {}, "IForest" : {}} model_names = [ "LOF" , "IForest"...scikit-learn.org/stable/auto_examples/miscellaneous/plot_outlier_detection_bench.html -
Feature discretization — scikit-learn 1.7.1 doc...
dataset 1 --------- LogisticRegression: 0.40 LinearSVC: 0.40 KBinsDiscretizer...np . logspace ( - 1 , 1 , 3 ), }, ), ( make_pipeline ( StandardScaler...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_classification.html -
Target Encoder’s Internal Cross fitting — sciki...
fit_transform (( y + noise ) . reshape ( - 1 , 1 )) # Remove the linear relationship...s ], axis = 1 , ), columns = [ "informative" , "shuffled" , "near_unique"...scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder_cross_val.html -
Support Vector Regression (SVR) using linear an...
True , ) fig . text ( 0.5 , 0.04 , "data" , ha = "center" , va...va = "center" ) fig . text ( 0.06 , 0.5 , "target" , ha = "center"...scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html