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HuberRegressor — scikit-learn 1.8.0 documentation
epsilon = 1.35 , max_iter = 100 , alpha =...Parameters : epsilon float, default=1.35 The parameter epsilon controls...scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html -
shuffle — scikit-learn 1.8.0 documentation
1.], [1., 0.]]) >>> y array([2, 1, 0]) >>>...>>> X = np . array ([[ 1. , 0. ], [ 2. , 1. ], [ 0. , 0. ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.shuffle.html -
extract_patches_2d — scikit-learn 1.8.0 documen...
max_patches is a float between 0 and 1, it is taken to be a proportion...patches: >>> print ( patches [ 1 ]) [[[174 201 231] [174 201 231]]...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.extract_patches_2d.html -
OrthogonalMatchingPursuitCV — scikit-learn 1.8....
means 1 unless in a joblib.parallel_backend context. -1 means...all strings. Added in version 1.0. See also orthogonal_mp Solves...scikit-learn.org/stable/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html -
Nested versus non-nested cross-validation — sci...
= { "C" : [ 1 , 10 , 100 ], "gamma" : [ 0.01 , 0.1 ]} # We will...Iris Dataset" , x = 0.5 , y = 1.1 , fontsize = "15" , ) # Plot...scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html -
Comparison of F-test and mutual information — s...
1 * N(0, 1), that is the third feature...sin ( 6 * np . pi * X [:, 1 ]) + 0.1 * np . random . randn ( 1000...scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html -
Marvel Reference for 2.x and 1.x | Elastic
x and 1.x: 2.1 Marvel Reference for 2.x and 1.x: 2.0 Marvel...Reference for 2.x and 1.x Marvel Reference for 2.x and 1.x: 2.4 Marvel...www.elastic.co/guide/en/marvel/index.html -
Selecting dimensionality reduction with Pipelin...
n_jobs=1, param_grid=[{'classify__C': [1, 10, 100, 1000],...`n_jobs` default changed from 1 to None 1 refit refit: bool, str, or...scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html -
Face completion with a multi-output estimators ...
shape [ 1 ] # Upper half of the faces X_train...X_train = train [:, : ( n_pixels + 1 ) // 2 ] # Lower half of the faces...scikit-learn.org/stable/auto_examples/miscellaneous/plot_multioutput_face_completion.html -
Factor Analysis (with rotation) to visualize pa...
vmin =- 1 , vmax = 1 ) ax . set_xticks ([ 0 , 1 , 2 , 3 ])...set_xticks ([ 0 , 1 ]) ax . set_xticklabels ([ "Comp. 1" , "Comp. 2"...scikit-learn.org/stable/auto_examples/decomposition/plot_varimax_fa.html