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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 -
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 -
multilabel_confusion_matrix — scikit-learn 1.7....
array([[[1, 0], [0, 1]], [[1, 0], [0, 1]], [[0, 1], [1, 0]]]) Multiclass...array([[[3, 1], [0, 2]], [[5, 0], [1, 0]], [[2, 1], [1, 2]]]) On...scikit-learn.org/stable/modules/generated/sklearn.metrics.multilabel_confusion_matrix.html -
Lagged features for time series forecasting — s...
1 ± 1.1" "19.9 ± 1.6" "22.7 ± 3.1" "quantile 5"..."39.9 ± 3.2" "17.1 ± 1.1" "19.9 ± 1.6" "22.7 ± 3.1" "quantile 95"...scikit-learn.org/stable/auto_examples/applications/plot_time_series_lagged_features.html -
Robust vs Empirical covariance estimate — sciki...
5 )[ 1 : - 1 ], ) ) . astype ( int ) # definition...= 11 ) plt . subplot ( 2 , 1 , 1 ) lw = 2 plt . errorbar ( range_n_outliers...scikit-learn.org/stable/auto_examples/covariance/plot_robust_vs_empirical_covariance.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 -
completeness_score — scikit-learn 1.7.0 documen...
1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Non-perfect labelings...completeness_score ([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 0 , 1 ])) 0.0 >>> print...scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html -
f1_score — scikit-learn 1.7.0 documentation
[ 1 , 1 , 1 ], [ 0 , 1 , 1 ]] >>> y_pred = [[...[[ 0 , 0 , 0 ], [ 1 , 1 , 1 ], [ 1 , 1 , 0 ]] >>> f1_score ( y_true...scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html -
confusion_matrix — scikit-learn 1.7.0 documenta...
1 , 0 , 1 ], [ 1 , 1 , 1 , 0 ]) . ravel ()...negatives is \(C_{1,0}\) , true positives is \(C_{1,1}\) and false...scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html -
Decision Tree Regression — scikit-learn 1.7.0 d...
scatter ( y_1 [:, 0 ], y_1 [:, 1 ], c = "cornflowerblue"...RandomState ( 1 ) X = np . sort ( 5 * rng . rand ( 80 , 1 ), axis =...scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html