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RepeatedKFold — scikit-learn 1.7.1 documentation
np . array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]]) >>>...np . array ([ 0 , 0 , 1 , 1 ]) >>> rkf = RepeatedKFold ( n_splits...scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedKFold.html -
locally_linear_embedding — scikit-learn 1.7.1 d...
solver. method {‘standard’, ‘hessian’, ‘modified’, ‘ltsa’}, default=’standard’...distances. eigen_solver {‘auto’, ‘arpack’, ‘dense’}, default=’auto’...scikit-learn.org/stable/modules/generated/sklearn.manifold.locally_linear_embedding.html -
extract_patches_2d — scikit-learn 1.7.1 documen...
load_sample_image ( "china.jpg" ) >>> print ( 'Image shape: {} ' . format...format ( one_image . shape )) Image shape: (427, 640, 3) >>> patches...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.extract_patches_2d.html -
incr_mean_variance_axis — scikit-learn 1.7.1 do...
todense () matrix([[8, 1, 2], [0, 0, 5], [0, 0, 0], [0, 0, 0]]) >>>...2 ... ) (array([1.33, 0.167, 1.17]), array([8.88, 0.139, 3.47]),...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.html -
linear_kernel — scikit-learn 1.7.1 documentation
= [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 ,..., 1 , 0 ]] >>> linear_kernel ( X , Y ) array([[0., 0.], [1.,...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.linear_kernel.html -
make_sparse_coded_signal — scikit-learn 1.7.1 d...
= 50 , ... n_components = 100 , ... n_features = 10 , ... n_nonzero_coefs...= 4 , ... random_state = 0 ... ) >>> data . shape (50, 10) >>>...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_coded_signal.html -
CompoundKernel — scikit-learn 1.7.1 documentation
= 2.0 )]) >>> print ( kernel . bounds ) [[-11.51292546 11.51292546]...CompoundKernel ( ... [ WhiteKernel ( noise_level = 3.0 ), RBF ( length_scale...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.CompoundKernel.html -
mutual_info_classif — scikit-learn 1.7.1 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 -
make_s_curve — scikit-learn 1.7.1 documentation
X . shape (100, 3) >>> t . shape (100,) Gallery examples # Comparison...sklearn.datasets. make_s_curve ( n_samples = 100 , * , noise = 0.0 , random_state...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_s_curve.html -
nan_euclidean_distances — scikit-learn 1.7.1 do...
nan_euclidean_distances ( X , [[ 0 , 0 ]]) array([[1. ], [1.41421356]]) On this...is: \[\sqrt{\frac{4}{2}((3-1)^2 + (6-5)^2)}\] If all the coordinates...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html