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cosine_distances — scikit-learn 1.7.1 documenta...
0 , 0 ], [ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]]...cosine_distances ( X , Y ) array([[1. , 1. ], [0.422, 0.183]]) On this...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_distances.html -
get_scorer_names — scikit-learn 1.7.1 documenta...
[: 3 ] ['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score']...get_scorer_names () >>> type ( all_scorers ) <class 'list'> >>> all_scorers...scikit-learn.org/stable/modules/generated/sklearn.metrics.get_scorer_names.html -
neighbors.rst.txt
, 0.], [1., 1., 0., 0., 0., 0.], [0., 1., 1., 0., 0., 0.], [0.,...= np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3,...scikit-learn.org/stable/_sources/modules/neighbors.rst.txt -
7.5. Unsupervised dimensionality reduction — sc...
heavily used. 7.5.1. PCA: principal component analysis # decomposition.PCA...projections 7.5.3. Feature agglomeration # cluster.FeatureAgglomeration...scikit-learn.org/stable/modules/unsupervised_reduction.html -
plot_pca_iris.rst.txt
'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename',...three types. Plot a PCA representation ---------- Let's apply a...scikit-learn.org/stable/_sources/auto_examples/decomposition/plot_pca_iris.rst.txt -
OrdinalEncoder — scikit-learn 1.7.1 documentation
([[ 1 , 0 ], [ 0 , 1 ]]) array([['Male', 1], ['Female', 2]], dtype=object)...]]) array([[0., 2.], [1., 0.]]) >>> enc . inverse_transform ([[...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html -
cross_validate — scikit-learn 1.7.1 documentation
instead. E.g.: cross_validate(..., params={'groups': groups}) . scoring...instance (e.g., GroupKFold ). Changed in version 1.4: groups can...scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html -
GroupKFold — scikit-learn 1.7.1 documentation
, 10 ], [ 11 , 12 ]]) >>> y = np . array ([ 1 , 2 , 3 , 4 , 5...5 , 6 ]) >>> groups = np . array ([ 0 , 0 , 2 , 2 , 3 , 3 ]) >>>...scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupKFold.html -
affinity_propagation — scikit-learn 1.7.1 docum...
([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2 ], [ 4 , 4 ],...array([0, 3]) >>> labels array([0, 0, 0, 1, 1, 1]) Gallery examples...scikit-learn.org/stable/modules/generated/sklearn.cluster.affinity_propagation.html -
ClassifierMixin — scikit-learn 1.7.1 documentation
. fit ( X , y ) . predict ( X ) array([1, 1, 1]) >>> estimator...param = 1 ): ... self . param = param ... def fit ( self , X , y =...scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html