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L1-based models for Sparse Signals — scik...
linspace ( - 2 , 2 , n_samples ) freqs = 2 * np . pi * np ....time_step + 2 * ( rng . random_sample () - 0.5 )) X [:, i ] += 0.2 * rng...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.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 -
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 -
3.4. Metrics and scoring: quantifying the quali...
= [ - 2 , - 2 , - 2 ] >>> y_pred = [ - 2 , - 2 , - 2...= [ - 2 , - 2 , - 2 ] >>> y_pred = [ - 2 , - 2 , - 2...scikit-learn.org/stable/modules/model_evaluation.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 -
make_gaussian_quantiles — scikit-learn 1....
int64(2), np.int64(0), np.int64(1), np.int64(0), np.int64(2)] Gallery...n_samples = 100 , n_features = 2 , n_classes = 3 , shuffle = True...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_gaussian_quantiles.html -
ShuffleSplit — scikit-learn 1.8.0 documen...
2 , 1 , 2 , 1 , 2 ]) >>> rs = ShuffleSplit...Test: index=[5 2] Fold 1: Train: index=[4 0 2 5] Test: index=[1...scikit-learn.org/stable/modules/generated/sklearn.model_selection.ShuffleSplit.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 -
Plot randomly generated multilabel dataset R...
1500 * p_c ** 2 , color = COLORS . take ([ 1 , 2 , 4 ]), ) ax...means the class is present: 1 2 3 Color Y N N Red N Y N Blue N...scikit-learn.org/stable/auto_examples/datasets/plot_random_multilabel_dataset.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