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partial_dependence — scikit-learn 1.7.2 documen...
None , response_method = 'auto' , percentiles = (0.05, 0.95) ,...feature (e.g. [0] ) or pair of interacting features (e.g. [(0, 1)]...scikit-learn.org/stable/modules/generated/sklearn.inspection.partial_dependence.html -
ConstantKernel — scikit-learn 1.7.2 documentation
True ) (array([606.1]), array([0.248])) __call__ ( X , Y = None...constant_value = 1.0 , constant_value_bounds = (1e-05, 100000.0) ) [source]...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html -
chi2 — scikit-learn 1.7.2 documentation
1 ], ... [ 6 , 6 , 2 ], ... [ 1 , 4 , 0 ], ... [ 0 , 0 , 0 ]])...np . array ([[ 1 , 1 , 3 ], ... [ 0 , 1 , 5 ], ... [ 5 , 4 ,...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html -
mutual_info_classif — scikit-learn 1.7.2 docume...
y ) array([0.589, 0.107, 0.196, 0.0968 , 0., 0. , 0. , 0. , 0....Sets”. PLoS ONE 9(2), 2014. [ 4 ] L. F. Kozachenko, N. N. Leonenko,...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_classif.html -
RegressorMixin — scikit-learn 1.7.2 documentation
estimator . fit ( X , y ) . predict ( X ) array([0, 0, 0]) >>> estimator...of squares ((y_true - y_true.mean()) ** 2).sum() . The best possible...scikit-learn.org/stable/modules/generated/sklearn.base.RegressorMixin.html -
make_column_transformer — scikit-learn 1.7.2 do...
er ( ... ( StandardScaler (), [ 'numerical_column' ]), ... (...['numerical_column']), ('onehotencoder', OneHotEncoder(...), ['categorical_column'])])...scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_transformer.html -
EmpiricalCovariance — scikit-learn 1.7.2 docume...
covariance_ array([[0.7569, 0.2818], [0.2818, 0.3928]]) >>> cov . location_...sqrt(tr(A^t.A)) - ‘spectral’: sqrt(max(eigenvalues(A^t.A)) where...scikit-learn.org/stable/modules/generated/sklearn.covariance.EmpiricalCovariance.html -
MinCovDet — scikit-learn 1.7.2 documentation
real_cov = np . array ([[ .8 , .3 ], ... [ .3 , .4 ]]) >>> rng =...>>> cov . covariance_ array([[0.7411, 0.2535], [0.2535, 0.3053]])...scikit-learn.org/stable/modules/generated/sklearn.covariance.MinCovDet.html -
get_data_home — scikit-learn 1.7.2 documentation
get_data_home () >>> os . path . exists ( data_home_path ) True Gallery...examples # Out-of-core classification of text documents Out-of-core...scikit-learn.org/stable/modules/generated/sklearn.datasets.get_data_home.html -
load_iris — scikit-learn 1.7.2 documentation
data . target [[ 10 , 25 , 50 ]] array([0, 0, 1]) >>> list ( data...data . target_names ) [np.str_('setosa'), np.str_('versicolor'),...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html