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MiniBatchKMeans — scikit-learn 1.7.2 documentation
generate. init {‘k-means++’, ‘random’}, callable or array-like of...= 'k-means++' , max_iter = 100 , batch_size = 1024 , verbose =...scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html -
LatentDirichletAllocation — scikit-learn 1.7.2 ...
evaluate_every = -1 , total_samples = 1000000.0 , perp_tol = 0.1 , mean_change_tol...model.components_ / model.components_.sum(axis=1)[:, np.newaxis]...scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html -
FastICA — scikit-learn 1.7.2 documentation
to ‘unit-variance’ in 1.3. fun {‘logcosh’, ‘exp’, ‘cube’} or...neg-entropy. Could be either ‘logcosh’, ‘exp’, or ‘cube’. You...scikit-learn.org/stable/modules/generated/sklearn.decomposition.FastICA.html -
3.3. Tuning the decision threshold for class pr...
, 0.06 ], [0.0416, 0.9583], [0.0416, 0.9583]]) >>> classifier...make_classification ( ... n_samples = 1_000 , weights = [ 0.1 , 0.9 ], random_state...scikit-learn.org/stable/modules/classification_threshold.html -
reconstruct_from_patches_2d — scikit-learn 1.7....
patch_size = ( 10 , 10 )) >>> print ( 'Patches shape: {} ' . format (...( image_patches . shape )) Patches shape: (263758, 10, 10, 3)...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.reconstruct_from_patch... -
DictVectorizer — scikit-learn 1.7.2 documentation
>>> X array([[2., 0., 1.], [0., 1., 3.]]) >>> v . inverse_transform...inverse_transform ( X ) == [{ 'bar' : 2.0 , 'foo' : 1.0 }, ... { 'baz' : 1.0...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html -
cross_val_score — scikit-learn 1.7.2 documentation
instead. E.g.: cross_val_score(..., params={'groups': groups})...print ( cross_val_score ( lasso , X , y , cv = 3 )) [0.3315057...scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html -
GaussianNB — scikit-learn 1.7.2 documentation
- 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2 , 1...1 ], [ 3 , 2 ]]) >>> Y = np . array ([ 1 , 1 , 1 , 2 , 2 , 2...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html -
plot_classifier_comparison.ipynb
x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n y_min,...GaussianProcessClass(1.0 * RBF(1.0), random_state=42),\n DecisionTreeClassifi(max_depth=5,...scikit-learn.org/stable/_downloads/3438aba177365cb595921cf18806dfa7/plot_classifier_comparison.ipynb -
load_digits — scikit-learn 1.7.2 documentation
cmap = "gray" ) <...> >>> plt . show () Gallery examples # Recognizing...load_digits () >>> print ( digits . data . shape ) (1797, 64) >>> import...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html