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sklearn.ensemble — scikit-learn 1.5.2 documenta...
Ensemble-based methods for classification, regression and anomaly detection. User guide. See the Ensembles: Gradient boosting, random forests, bagging, voting, stacking section for further details.scikit-learn.org/stable/api/sklearn.ensemble.html -
gen_batches — scikit-learn 1.5.2 documentation
Skip to main content Back to top Ctrl + K GitHub gen_batches # sklearn.utils. gen_batches ( n , batch_size , * , min_...scikit-learn.org/stable/modules/generated/sklearn.utils.gen_batches.html -
10. Common pitfalls and recommended practices —...
n_features = 1 , noise = 1 ) >>> X_train , X_test ,...applies to using None . 10.3.1.1. Estimators # Passing instances...scikit-learn.org/stable/common_pitfalls.html -
Gaussian process classification (GPC) on iris d...
y ) kernel = 1.0 * RBF ([ 1.0 , 1.0 ]) gpc_rbf_anisotropic...() - 1 , X [:, 0 ] . max () + 1 y_min , y_max = X [:, 1 ] . min...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_iris.html -
Classification of text documents using sparse f...
the mean squared error on {-1, 1} encoded targets, one for each...n_features: { X_train . shape [ 1 ] } " ) print ( f "vectorize testing...scikit-learn.org/stable/auto_examples/text/plot_document_classification_20newsgroups.html -
Comparing different clustering algorithms on to...
"connectivity matrix is [0-9]{1,2}" + " > 1. Completing it to avoid...= n_samples , cluster_std = [ 1.0 , 2.5 , 0.5 ], random_state...scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html -
Face completion with a multi-output estimators ...
shape [ 1 ] # Upper half of the faces X_train...X_train = train [:, : ( n_pixels + 1 ) // 2 ] # Lower half of the faces...scikit-learn.org/stable/auto_examples/miscellaneous/plot_multioutput_face_completion.html -
SVM: Separating hyperplane for unbalanced class...
clusters of random points n_samples_1 = 1000 n_samples_2 = 100 centers...2.0 , 2.0 ]] clusters_std = [ 1.5 , 0.5 ] X , y = make_blobs (...scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html -
Faces recognition example using eigenfaces and ...
1 )[ - 1 ] true_name = target_names...y_test [ i ]] . rsplit ( " " , 1 )[ - 1 ] return "predicted: %s \n...scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html -
SGD: Maximum margin separating hyperplane — sci...
linspace ( - 1 , 5 , 10 ) yy = np . linspace ( - 1 , 5 , 10 ) X1...= p [ 0 ] levels = [ - 1.0 , 0.0 , 1.0 ] linestyles = [ "dashed"...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html