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contingency_matrix — scikit-learn 1.8.0 d...
2 , 2 ] >>> labels_pred = [ 1 , 0 , 2 , 1 , 0...0 , 2 ] >>> contingency_matrix ( labels_true , labels_pred...scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.contingency_matrix.html -
neighbors.rst.txt
[-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> nbrs...np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> kdt...scikit-learn.org/stable/_sources/modules/neighbors.rst.txt -
Joint feature selection with multi-task Lasso &...
2 , 2 ) plt . spy ( coef_multi_task_lasso_...)) times = np . linspace ( 0 , 2 * np . pi , n_tasks ) for k in...scikit-learn.org/stable/auto_examples/linear_model/plot_multi_task_lasso_support.html -
VotingRegressor — scikit-learn 1.8.0 docu...
float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...X = np . array ([[ 1 , 1 ], [ 2 , 4 ], [ 3 , 9 ], [ 4 , 16 ],...scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingRegressor.html -
lasso_path — scikit-learn 1.8.0 documenta...
it is: ( 1 / ( 2 * n_samples )) * || Y - XW ||^ 2 _Fro + alpha...X = np . array ([[ 1 , 2 , 3.1 ], [ 2.3 , 5.4 , 4.3 ]]) . T >>>...scikit-learn.org/stable/modules/generated/sklearn.linear_model.lasso_path.html -
randomized_svd — scikit-learn 1.8.0 docum...
2), (2,), (2, 4)) On this page This Page...increase this parameter up to 2*k - n_components where k is the...scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.randomized_svd.html -
CCA — scikit-learn 1.8.0 documentation
[ 2. , 2. , 2. ], [ 3. , 5. , 4. ]] >>>...y = [[ 0.1 , - 0.2 ], [ 0.9 , 1.1 ], [ 6.2 , 5.9 ], [ 11.9 ,...scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.CCA.html -
KFold — scikit-learn 1.8.0 documentation
2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]]) >>>...1 , 2 , 3 , 4 ]) >>> kf = KFold ( n_splits = 2 ) >>>...scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html -
L1-based models for Sparse Signals — scik...
linspace ( - 2 , 2 , n_samples ) freqs = 2 * np . pi * np ....time_step + 2 * ( rng . random_sample () - 0.5 )) X [:, i ] += 0.2 * rng...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.html -
Feature agglomeration vs. univariate selection ...
selection # This example compares 2 dimensionality reduction strategies:.... randn ( n_samples , size ** 2 ) for x in X : # smooth data x...scikit-learn.org/stable/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection....