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2.4. Biclustering — scikit-learn 1.7.0 document...
array ([ 1 , 2 ]) >>> data [ rows , columns ] array([[ 1, 2], [21,...to find heavy subgraphs. 2.4.1.1. Mathematical formulation # An...scikit-learn.org/stable/modules/biclustering.html -
8. Dataset loading utilities — scikit-learn 1.7...
1. Toy datasets 8.1.1. Iris plants dataset 8.1.2. Diabetes...digits dataset 8.1.4. Linnerrud dataset 8.1.5. Wine recognition dataset...scikit-learn.org/stable/datasets.html -
Plot randomly generated multilabel dataset — sc...
1 ], color = COLORS . take (( Y * [ 1 , 2 , 4 ]) ...., n_labels = 1 ) ax1 . set_title ( "n_labels=1, length=50" )...scikit-learn.org/stable/auto_examples/datasets/plot_random_multilabel_dataset.html -
Two-class AdaBoost — scikit-learn 1.7.0 documen...
random_state = 1 ) X2 , y2 = make_gaussian_quantiles...es ( mean = ( 3 , 3 ), cov = 1.5 , n_samples = 300 , n_features...scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_twoclass.html -
Early stopping in Gradient Boosting — scikit-le...
axes [ 1 ] . set_xlabel ( "Boosting Iterations" ) axes [ 1 ] . set_ylabel...(Validation)" ) axes [ 1 ] . set_yscale ( "log" ) axes [ 1 ] . legend ()...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_early_stopping.html -
additive_chi2_kernel — scikit-learn 1.7.0 docum...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...additive_chi2_kernel ( X , Y ) array([[-1., -2.], [-2., -1.]]) On this page This...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.additive_chi2_kernel.html -
Ordinary Least Squares and Ridge Regression — s...
1 ] . T y_train = [ 0.5 , 1 ] X_test = np . c_...this_X = 0.1 * np . random . normal ( size = ( 2 , 1 )) + X_train...scikit-learn.org/stable/auto_examples/linear_model/plot_ols_ridge.html -
balanced_accuracy_score — scikit-learn 1.7.0 do...
1 , 0 , 0 , 1 , 0 ] >>> y_pred = [ 0 , 1 , 0 , 0 ,...each class. The best value is 1 and the worst value is 0 when...scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html -
SGD: Weighted samples — scikit-learn 1.7.0 docu...
) + [ 1 , 1 ], np . random . randn ( 10 , 2 )] y = [ 1 ] * 10...10 + [ - 1 ] * 10 sample_weight = 100 * np . abs ( np . random...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html -
sklearn.kernel_approximation — scikit-learn 1.7...
Approximate kernel feature maps based on Fourier transforms and count sketches. User guide. See the Kernel Approximation section for further details.scikit-learn.org/stable/api/sklearn.kernel_approximation.html