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6.1. Pipelines and composite estimators — sciki...
must be transformers (i.e. must have a transform...integer array, a slice, a boolean mask, or with a make_column_selector...scikit-learn.org/stable/modules/compose.html -
Compressive sensing: tomography reconstruction ...
logical_and ( mask > mask . mean (), mask_outer ) return np...list ( w [ mask ]) camera_inds += list ( inds [ mask ] + i * l_x...scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html -
Kernel Density Estimate of Species Distribution...
:: 5 ] land_mask = ( land_reference > - 9999...ravel ()]) . T xy = xy [ land_mask ] xy *= np . pi / 180.0 # Plot...scikit-learn.org/stable/auto_examples/neighbors/plot_species_kde.html -
sklearn.feature_extraction.image.grid_to_graph ...
mask = mask ) >>> print ( graph ) (0,...grid_to_graph ( n_x , n_y , n_z=1 , * , mask=None , return_as=<class 'sc...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.grid_to_graph.html -
Pixel importances with a parallel forest of tre...
mask = y < 5 X = X [ mask ] y = y [ mask ] A random...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances_faces.html -
4.1. Partial Dependence and Individual Conditio...
of input features of interest must be small (usually, one or two)...categorical features or a boolean mask. The graphical representation...scikit-learn.org/stable/modules/partial_dependence.html -
Robust linear model estimation using RANSAC — s...
) inlier_mask = ransac . inlier_mask_ outlier_mask = np . logical_not...scatter ( X [ inlier_mask ], y [ inlier_mask ], color = "yellowgreen"...scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html -
sklearn.ensemble.GradientBoostingRegressor — sc...
values must be in the range [2, inf) . If float, values must be in...values must be in the range [1, inf) . If float, values must be in...scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html -
Multiclass Receiver Operating Characteristic (R...
= a_mask [ ab_mask ] b_true = b_mask [ ab_mask ] idx_a = np ....label_b ab_mask = np . logical_or ( a_mask , b_mask ) a_true =...scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html -
sklearn.preprocessing.label_binarize — scikit-l...
Value with which negative labels must be encoded. pos_label int, default=1...Value with which positive labels must be encoded. sparse_output bool,...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.label_binarize.html