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make_classification — scikit-learn 1.5.2 docume...
class_sep = 1.0 , hypercube = True , shift = 0.0 , scale = 1.0 , shuffle...[np.int64(0), np.int64(0), np.int64(1), np.int64(1), np.int64(0)] Gallery...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html -
spectral_clustering — scikit-learn 1.5.2 docume...
array ([[ 1 , 1 ], [ 2 , 1 ], [ 1 , 0 ], ... [ 4 , 7...random_state = 0 ... ) array([1, 1, 1, 0, 0, 0]) Gallery examples...scikit-learn.org/stable/modules/generated/sklearn.cluster.spectral_clustering.html -
PolynomialFeatures — scikit-learn 1.5.2 documen...
fit_transform ( X ) array([[ 1., 0., 1., 0., 0., 1.], [ 1., 2., 3., 4., 6.,...) array([[ 1., 0., 1., 0.], [ 1., 2., 3., 6.], [ 1., 4., 5., 20.]])...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html -
enet_path — scikit-learn 1.5.2 documentation
l1_ratio * || w || _1 + 0.5 * alpha * ( 1 - l1_ratio ) * || w...0.56...], [ 0. , 1.12..., 0.61...], [-0. , -2.12..., -1.12...], [ 0....scikit-learn.org/stable/modules/generated/sklearn.linear_model.enet_path.html -
RepeatedKFold — scikit-learn 1.5.2 documentation
array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]])...>>> y = np . array ([ 0 , 0 , 1 , 1 ]) >>> rkf = RepeatedKFold (...scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedKFold.html -
chi2 — scikit-learn 1.5.2 documentation
array ([[ 1 , 1 , 3 ], ... [ 0 , 1 , 5 ], ... [ 5 , 4 , 1 ], ......y = np . array ([ 1 , 1 , 0 , 0 , 2 , 2 ]) >>> chi2_stats , p_values...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html -
RBFSampler — scikit-learn 1.5.2 documentation
[ 1 , 1 ], [ 1 , 0 ], [ 0 , 1 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>>...in version 1.2: The option "scale" was added in 1.2. n_components...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.RBFSampler.html -
f_regression — scikit-learn 1.5.2 documentation
r_regression values lie in [-1, 1] and can thus be negative. f_regression...set to 0.0 . Added in version 1.1. Returns : f_statistic ndarray...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html -
1.11. Ensembles: Gradient boosting, random fore...
Gradient Boosting models 1.11.1.1.1. Usage # Most of the parameters...= [[ 1 , 0 ], ... [ 1 , 0 ], ... [ 1 , 0 ], ... [ 0 , 1 ]] >>>...scikit-learn.org/stable/modules/ensemble.html -
LatentDirichletAllocation — scikit-learn 1.5.2 ...
evaluate_every = -1 , total_samples = 1000000.0 , perp_tol = 0.1 , mean_change_tol...None, defaults to 1 / n_components . In [1] , this is called...scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html