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SGD: Weighted samples — scikit-learn 1.8.0 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 -
fast_logdet — scikit-learn 1.8.0 document...
1 ], [ 2 , 8 ]]) >>> fast_logdet...scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.fast_logdet.html -
L1-based models for Sparse Signals — scikit-lea...
1 , n_samples ) y += 0.2 * rng . normal ( 0 , 1 , n_samples...linthresh = 10e-4 , vmin =- 1 , vmax = 1 ), cbar_kws = { "label"...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.html -
Detection error tradeoff (DET) curve — scikit-l...
random_state = 1 , n_clusters_per_class = 1 , ) X_train , X_test...n_estimators = 10 , max_features = 1 , random_state = 0 ), "Non-informative...scikit-learn.org/stable/auto_examples/model_selection/plot_det.html -
KernelDensity — scikit-learn 1.8.0 docume...
log_density array([-1.52955942, -1.51462041, -1.60244657]) fit (...KernelDensity ( * , bandwidth = 1.0 , algorithm = 'auto' , kernel...scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html -
Kernel Density Estimate of Species Distribution...
subplot ( 1 , 2 , i + 1 ) # construct a kernel density...by Phillips et. al. (2006) [ 1 ] . If available, the example...scikit-learn.org/stable/auto_examples/neighbors/plot_species_kde.html -
homogeneity_score — scikit-learn 1.8.0 document...
1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Non-perfect labelings...homogeneity_score ([ 0 , 0 , 1 , 1 ], [ 0 , 0 , 1 , 2 ])) 1.000000 >>> print...scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_score.html -
maxabs_scale — scikit-learn 1.8.0 documentation
1 , 2 ], [ - 1 , 0 , 1 ]] >>> maxabs_scale...column independently array([[-1. , 1. , 1. ], [-0.5, 0. , 0.5]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.maxabs_scale.html -
Two-class AdaBoost — scikit-learn 1.8.0 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 -
TfidfTransformer — scikit-learn 1.8.0 documenta...
array([[1, 1, 1, 1, 0, 1, 0, 0], [1, 2, 0, 1, 1, 1, 0, 0], [1, 0,...0, 0, 1, 0, 1, 1, 1], [1, 1, 1, 1, 0, 1, 0, 0]]) >>> pipe [ 'tfid'...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html