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ExpSineSquared — scikit-learn 1.8.0 documentation
ExpSineSquared ( length_scale = 1.0 , periodicity = 1.0 , length_scale_bounds...ExpSineSquared ( length_scale = 1 , periodicity = 1 ) >>> gpr = GaussianProcessRegre...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ExpSineSquared.html -
coverage_error — scikit-learn 1.8.0 documentation
y_true = [[ 1 , 0 , 0 ], [ 0 , 1 , 1 ]] >>> y_score = [[ 1 , 0 , 0...[ 0 , 1 , 1 ]] >>> coverage_error ( y_true , y_score ) 1.5 On...scikit-learn.org/stable/modules/generated/sklearn.metrics.coverage_error.html -
additive_chi2_kernel — scikit-learn 1.8.0 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 -
MultiOutputRegressor — scikit-learn 1.8.0...
means 1 unless in a joblib.parallel_backend context. -1 means...X [[ 0 ]]) array([[176, 35.1, 57.1]]) fit ( X , y , sample_weight...scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputRegressor.html -
make_friedman1 — scikit-learn 1.8.0 documentation
Annals of Statistics 19 (1), pages 1-67, 1991. [ 2 ] L. Breiman,...[source] # Generate the “Friedman #1” regression problem. This dataset...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman1.html -
ridge_regression — scikit-learn 1.8.0 documenta...
0 ] - 1.0 * X [:, 1 ] + 0.1 * rng . standard_normal...random_state = 0 ) >>> coef array([ 1.97, -1., -2.69e-3, -9.27e-4 ]) >>>...scikit-learn.org/stable/modules/generated/sklearn.linear_model.ridge_regression.html -
KNeighborsTransformer — scikit-learn 1.8.0 docu...
() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]]) set_output...bors=1) >>> print ( neigh . kneighbors ([[ 1. , 1. , 1. ]]))...scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsTransformer.html -
robust_scale — scikit-learn 1.8.0 documen...
independently array([[-1., 1., 1.], [ 1., -1., -1.]]) >>>...>>> X = [[ - 2 , 1 , 2 ], [ - 1 , 0 , 1 ]] >>> robust_scale...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.robust_scale.html -
r2_score — scikit-learn 1.8.0 documentation
1 ], [ - 1 , 1 ], [ 7 , - 6 ]] >>> y_pred...cross-validation). Added in version 1.1. Returns : z float or ndarray...scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html -
RadiusNeighborsRegressor — scikit-learn 1.8.0 d...
() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) score (...[[ 0 ], [ 1 ], [ 2 ], [ 3 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>> from...scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.html