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sort_graph_by_row_values — scikit-learn 1.7.2 d...
2. ], ... [ 1. , 2. , 0. ]]) >>> X . data...data array([3., 1., 3., 2., 1., 2.]) >>> X_ = sort_graph_by_row_values...scikit-learn.org/stable/modules/generated/sklearn.neighbors.sort_graph_by_row_values.html -
User Guide — scikit-learn 1.7.2 documentation
Mixture 2.2. Manifold learning 2.2.1. Introduction 2.2.2. Isomap...Isomap 2.2.3. Locally Linear Embedding 2.2.4. Modified Locally Linear...scikit-learn.org/stable/user_guide.html -
type_of_target — scikit-learn 1.7.2 documentation
2.0 ]) 'binary' >>> type_of_target ([ 1 , 0 , 2 ]) 'multiclass'...> 1. ‘binary’: y contains <= 2 discrete values and is 1d or a...scikit-learn.org/stable/modules/generated/sklearn.utils.multiclass.type_of_target.html -
Principal Component Regression vs Partial Least...
n_samples ) / 2 fig , axes = plt . subplots ( 1 , 2 , figsize =...target. pca_2 = make_pipeline ( PCA ( n_components = 2 ), LinearRegression...scikit-learn.org/stable/auto_examples/cross_decomposition/plot_pcr_vs_pls.html -
Combine predictors using stacking — scikit-lear...
subplots ( 2 , 2 , figsize = ( 9 , 7 )) axs =...1 , encoded_missing_value =- 2 , ) num_tree_processor = SimpleImputer...scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html -
Visualization of MLP weights on MNIST — scikit-...
44139186 Iteration 2, loss = 0.19174891 Iteration 3,...= 1 , learning_rate_init = 0.2 , ) # this example won't converge...scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html -
Lasso on dense and sparse data — scikit-learn 1...
values lower than 2.5 with 0s Xs [ Xs < 2.5 ] = 0.0 # create...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.html -
PLSSVD — scikit-learn 1.7.2 documentation
[ 2. , 2. , 2. ], ... [ 2. , 5. , 4. ]]) >>>...([[ 0.1 , - 0.2 ], ... [ 0.9 , 1.1 ], ... [ 6.2 , 5.9 ], ... [...scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSSVD.html -
RegressorChain — scikit-learn 1.7.2 documentation
2 ], [ 1 , 1 ], [ 2 , 0 ]] >>> chain = RegressorChain...predict ( X ) array([[0., 2.], [1., 1.], [2., 0.]]) fit ( X , Y ,...scikit-learn.org/stable/modules/generated/sklearn.multioutput.RegressorChain.html -
as_float_array — scikit-learn 1.7.2 documentation
2 , 2 ], dtype = np . int64 ) >>>...array ) array([0., 0., 1., 2., 2.]) On this page This Page...scikit-learn.org/stable/modules/generated/sklearn.utils.as_float_array.html