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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 -
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 -
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 -
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 -
SVM-Anova: SVM with univariate feature selectio...
score_stds = list () percentiles = ( 1 , 3 , 6 , 10 , 15 , 20 , 30 ,...scikit-learn.org/stable/auto_examples/svm/plot_svm_anova.html -
3.2. Tuning the hyper-parameters of an estimato...
loguniform(1, 100) can be used instead of [1, 10, 100] . Mirroring...reference to the literature. 3.2.1. Exhaustive Grid Search # The...scikit-learn.org/stable/modules/grid_search.html -
get_routing_for_object — scikit-learn 1.6.0 doc...
Added in version 1.3. Parameters : obj object If...scikit-learn.org/stable/modules/generated/sklearn.utils.metadata_routing.get_routing_for_object.html -
Cross-validation on diabetes Dataset Exercise —...
alphas [ - 1 ]]) (np.float64(0.0001), np.f...print ( "[fold {0} ] alpha: {1:.5f} , score: {2:.5f} " . format...scikit-learn.org/stable/auto_examples/exercises/plot_cv_diabetes.html -
incr_mean_variance_axis — scikit-learn 1.6.0 do...
(array([1.3..., 0.1..., 1.1...]), array([8.8..., 0.1..., 3.4...]),...array ([ 0 , 1 , 2 , 2 ]) >>> data = np . array ([ 8 , 1 , 2 , 5 ])...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.html