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Concatenating multiple feature extraction metho...
0s [CV 2/5; 2/18] START features__pca__n_components=1,...features__univ_select__k=1, svm__C=1 [CV 2/5; 2/18] END features__pca__n_components=1,...scikit-learn.org/stable/auto_examples/compose/plot_feature_union.html -
7.9. Transforming the prediction target (y) ...
y = [[ 2 , 3 , 4 ], [ 2 ], [ 0 , 1 , 3 ], [ 0 , 1 , 2 , 3 , 4...>>> lb . fit ([ 1 , 2 , 6 , 4 , 2 ]) LabelBinarizer() >>>...scikit-learn.org/stable/modules/preprocessing_targets.html -
TimeSeriesSplit — scikit-learn 1.8.0 docu...
2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 ,...1] Test: index=[2] Fold 2: Train: index=[0 1 2] Test: index=[3]...scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html -
inplace_swap_column — scikit-learn 1.8.0 ...
2 , 2 ]) >>> data = np . array ([ 8 , 2 , 5 ])...>>> indptr = np . array ([ 0 , 2 , 3 , 3 , 3 ]) >>> indices...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_column.html -
inplace_row_scale — scikit-learn 1.8.0 do...
2 , 5 , 6 ]) >>> scale = np . array ([ 2 , 3 ,...>>> indptr = np . array ([ 0 , 2 , 3 , 4 , 5 ]) >>> indices...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.inplace_row_scale.html -
hinge_loss — scikit-learn 1.8.0 documenta...
>>> pred_decision array([-2.18, 2.36, 0.09]) >>> hinge_loss...], [ 2 ], [ 3 ]]) >>> Y = np . array ([ 0 , 1 , 2 , 3...scikit-learn.org/stable/modules/generated/sklearn.metrics.hinge_loss.html -
StratifiedGroupKFold — scikit-learn 1.8.0...
index=[ 0 1 2 3 15 16] group=[1 1 2 2 8 8] Fold 2: Train: index=[...index=[ 0 1 2 3 7 8 9 10 11 15 16] group=[1 1 2 2 4 5 5 5 5 8...scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedGroupKFold.html -
shuffle — scikit-learn 1.8.0 documentation
[2., 1.], [1., 0.]]) >>> y array([2, 1, 0]) >>>...= np . array ([[ 1. , 0. ], [ 2. , 1. ], [ 0. , 0. ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.shuffle.html -
Robust covariance estimation and Mahalanobis di...
standard deviation equal to 2 and feature 2 has a standard deviation...n_features = 2 # generate Gaussian data of shape (125, 2) gen_cov...scikit-learn.org/stable/auto_examples/covariance/plot_mahalanobis_distances.html -
manhattan_distances — scikit-learn 1.8.0 ...
2 ], [ 3 , 4 ]], [[ 1 , 2 ], [ 0 , 3 ]]) array([[0., 2.],...manhattan_distances ([[ 3 ]], [[ 2 ]]) array([[1.]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.manhattan_distances.html