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Varying regularization in Multi-layer Perceptro...
n_features = 2 , n_redundant = 0 , n_informative = 2 , random_state.... random . RandomState ( 2 ) X += 2 * rng . uniform ( size =...scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_alpha.html -
DBSCAN — scikit-learn 1.8.0 documentation
2 ], [ 2 , 2 ], [ 2 , 3 ], ... [ 8 , 7 ],...memory complexity of \(O({n}^2)\) , which can occur when the...scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html -
TheilSenRegressor — scikit-learn 1.8.0 do...
float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...n_samples = 200 , n_features = 2 , noise = 4.0 , random_state =...scikit-learn.org/stable/modules/generated/sklearn.linear_model.TheilSenRegressor.html -
Custom refit strategy of a grid search with cro...
parameter candidate is displayed; - >2 : the score is also displayed;...pre_dispatch: int, or str, default='2*n_jobs' Controls the number of...scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_digits.html -
plot_multi_metric_evaluation.zip
range(2, 403, 20)}, scoring=scoring, refit="AUC", n_jobs=2, re...ax.plot( [ X_axis[best_index], ] * 2, [0, best_score], linestyle="-.",...scikit-learn.org/stable/_downloads/535778bfbc9b4881da3e662bc2ea8484/plot_multi_metric_evaluation.zip -
cross_validate — scikit-learn 1.8.0 docum...
pre_dispatch = '2*n_jobs' , return_train_score =...pre_dispatch int or str, default=’2*n_jobs’ Controls the number of...scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html -
BayesianRidge — scikit-learn 1.8.0 docume...
[ 2 , 2 ]], [ 0 , 1 , 2 ]) BayesianRidge() >>>...float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...scikit-learn.org/stable/modules/generated/sklearn.linear_model.BayesianRidge.html -
FeatureUnion — scikit-learn 1.8.0 documen...
n_components = 2 ))]) >>> X = [[ 0. , 1. , 3 ], [ 2. , 2. , 5...parameters. Added in version 1.2. n_features_in_ int Number of...scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html -
max_error — scikit-learn 1.8.0 documentation
2 , 7 , 1 ] >>> y_pred = [ 4 , 2 , 7 , 1 ]...scikit-learn.org/stable/modules/generated/sklearn.metrics.max_error.html -
plot_classifier_comparison.rst.txt
make_classification( n_features=2, n_redundant=0, n_informative=2, random_state=1,...rng = np.random.RandomState(2) X += 2 * rng.uniform(size=X.shape)...scikit-learn.org/stable/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt