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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 -
assert_all_finite — scikit-learn 1.7.1 document...
array ([ 1 , np . inf , np . nan , 4 ]) >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.assert_all_finite.html -
Sample pipeline for text feature extraction and...
array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00,...1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06]), 'vect__max_df':...scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_text_feature_extraction.html -
inplace_swap_row — scikit-learn 1.7.1 documenta...
1 ) >>> csr . todense () matrix([[0,...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_row.html -
Varying regularization in Multi-layer Perceptro...
logspace ( - 1 , 1 , 5 ) classifiers = [] names..., y_max = X [:, 1 ] . min () - 0.5 , X [:, 1 ] . max () + 0.5...scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_alpha.html -
Explicit feature map approximation for RBF kern...
reshape ( - 1 , data . shape [ 1 ]) # title for the plots...y_max]. plt . subplot ( 1 , 3 , i + 1 ) Z = clf . predict ( flat_grid...scikit-learn.org/stable/auto_examples/miscellaneous/plot_kernel_approximation.html -
Swiss Roll And Swiss-Hole Reduction — scikit-le...
) axs [ 1 ] . scatter ( sr_tsne [:, 0 ], sr_tsne [:, 1 ], c =...) axs [ 1 ] . scatter ( sh_tsne [:, 0 ], sh_tsne [:, 1 ], c =...scikit-learn.org/stable/auto_examples/manifold/plot_swissroll.html -
Lasso model selection via information criteria ...
lasso_lars_ic [ - 1 ] . criterion_ , n_samples , lasso_lars_ic [ - 1 ] . noise_variance_...lasso_lars_ic [ - 1 ] . alphas_ == lasso_lars_ic [ - 1 ] . alpha_ )[...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars_ic.html -
7.6. Random Projection — scikit-learn 1.7.1 doc...
}}} & & 1 / 2s\\ 0 &\text{with probability} & 1 - 1 / s \\ +...New York, NY, USA, 245-250. 7.6.1. The Johnson-Lindenstrauss lemma...scikit-learn.org/stable/modules/random_projection.html -
Poisson regression and non-normal loss — scikit...
Region 0 1.0 1 0.10000 D 5 0 55 50 B12 'Regular' 1217 R82 1 3.0 1...1 0.77000 D 5 0 55 50 B12 'Regular' 1217 R82 2 5.0 1 0.75000...scikit-learn.org/stable/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html