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clear_data_home — scikit-learn 1.7.2 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version clear_data_home # sklearn.datasets. clear_data_home (...scikit-learn.org/stable/modules/generated/sklearn.datasets.clear_data_home.html -
sklearn.cross_decomposition — scikit-learn 1.7....
Algorithms for cross decomposition. User guide. See the Cross decomposition section for further details.scikit-learn.org/stable/api/sklearn.cross_decomposition.html -
sklearn.discriminant_analysis — scikit-learn 1....
Linear and quadratic discriminant analysis. User guide. See the Linear and Quadratic Discriminant Analysis section for further details.scikit-learn.org/stable/api/sklearn.discriminant_analysis.html -
Post-tuning the decision threshold for cost-sen...
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])}, scoring='neg_log_loss')...scikit-learn.org/stable/auto_examples/model_selection/plot_cost_sensitive_learning.html -
t-SNE: The effect of various perplexity values ...
reshape ( - 1 , 1 ), yy . ravel () . reshape ( - 1 , 1 ), ] ) color...perplexities ): ax = subplots [ 1 ][ i + 1 ] t0 = time () tsne = manifold...scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html -
Illustration of Gaussian process classification...
kernels = [ 1.0 * RBF ( length_scale = 1.15 ), 1.0 * DotProduct...)[:, 1 ] Z = Z . reshape ( xx . shape ) plt . subplot ( 1 , 2...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html -
Ability of Gaussian process regression (GPR) to...
reshape ( - 1 , 1 ) y = target_generator ( X ,..., size = 20 ) . reshape ( - 1 , 1 ) y_train = target_generator...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy.html -
inplace_csr_row_normalize_l1 — scikit-learn 1.7...
1 , 2 , 3 ]) >>> data = np . array ([ 1.0 , 2.0 , 3.0...0. ], [0. , 0. , 1. , 0. ], [0. , 0. , 0. , 1. ]]) On this page...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normaliz... -
load_digits — scikit-learn 1.7.2 documentation
scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html -
Single estimator versus bagging: bias-variance ...
n_estimators - 1 : plt . legend ( loc = ( 1.1 , 0.5 )) plt ....axis = 1 )) ** 2 y_var = np . var ( y_predict , axis = 1 ) print...scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html