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Inductive Clustering — scikit-learn 1.6.1 docum...
cluster_std = [ 1.0 , 1.0 , 0.5 ], centers = [( - 5.... scatter ( X [:, 0 ], X [:, 1 ], c = color , alpha = alpha ,...scikit-learn.org/stable/auto_examples/cluster/plot_inductive_clustering.html -
3.1. Cross-validation: evaluating estimator per...
3] [0 1] [1 3] [0 2] [1 2] [0 3] [0 3] [1 2] [0 2] [1 3] [0 1]...>>> y = [ 1 , 1 , 1 , 2 , 2 , 2 ] >>> groups = [ 1 , 1 , 2 , 2...scikit-learn.org/stable/modules/cross_validation.html -
2.5. Decomposing signals in components (matrix ...
([[ 1 , 0 ], [ 1 , 6.1 ], [ 1 , 0 ], [ 1 , 4 ], [ 3.2 , 1 ], [...array ([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8...scikit-learn.org/stable/modules/decomposition.html -
Support Vector Regression (SVR) using linear an...
1.1 ), ncol = 1 , fancybox = True , shadow..., C = 100 , gamma = 0.1 , epsilon = 0.1 ) svr_lin = SVR ( kernel...scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html -
Related Projects — scikit-learn 1.6.1 documenta...
As of scikit-learn version 1.3.0, there is HDBSCAN . Pre-processing...encoders. As of scikit-learn version 1.3.0, there is TargetEncoder ....scikit-learn.org/stable/related_projects.html -
CountVectorizer — scikit-learn 1.6.1 documentation
[[0 1 1 1 0 0 1 0 1] [0 2 0 1 0 1 1 0 1] [1 0 0 1 1 0 1 1 1] [0...[[0 0 1 1 0 0 1 0 0 0 0 1 0] [0 1 0 1 0 1 0 1 0 0 1 0 0] [1 0 0...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html -
NearestNeighbors — scikit-learn 1.6.1 documenta...
() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]]) radius_neighbors...() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) set_params...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html -
resample — scikit-learn 1.6.1 documentation
1 , 1 , 1 , 1 , 1 ] >>> resample ( y , n_samples = 5 , replace...array([[1., 0.], [2., 1.], [1., 0.]]) >>> y array([0, 1, 0]) >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.resample.html -
TfidfTransformer — scikit-learn 1.6.1 documenta...
array([[1, 1, 1, 1, 0, 1, 0, 0], [1, 2, 0, 1, 1, 1, 0, 0], [1, 0,...0, 0, 1, 0, 1, 1, 1], [1, 1, 1, 1, 0, 1, 0, 0]]) >>> pipe [ 'tfid'...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html -
LabelBinarizer — scikit-learn 1.6.1 documentation
array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 1, 0]]) fit ( y ) [source]...array([1, 2, 4, 6]) >>> lb . transform ([ 1 , 6 ]) array([[1, 0,...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html