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cosine_distances — scikit-learn 1.8.0 documenta...
[ 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 -
cluster_optics_xi — scikit-learn 1.8.0 document...
1, 1, 1]) >>> clusters array([[0, 2],...min_samples int > 1 or float between 0 and 1 The same as the min_samples...scikit-learn.org/stable/modules/generated/sklearn.cluster.cluster_optics_xi.html -
compute_optics_graph — scikit-learn 1.8.0 docum...
1. , 1. , 4.12]) >>> reachability array([ inf, 3.16, 1.41,...1.41, 4.12, 1. , 5. ]) >>> predecessor array([-1, 0, 1, 5, 3, 2])...scikit-learn.org/stable/modules/generated/sklearn.cluster.compute_optics_graph.html -
DecisionTreeRegressor — scikit-learn 1.8.0 docu...
1: monotonic increase 0: no constraint -1: monotonic...scikit-learn 1.8 Release Highlights for scikit-learn 1.8 Decision...scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html -
power_transform — scikit-learn 1.8.0 documentation
'box-cox' )) [[-1.332 -0.707] [ 0.256 -0.707] [ 1.076 1.414]] Warning...Available methods are: ‘yeo-johnson’ [1] , works with positive and negative...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.power_transform.html -
SparseCoder — scikit-learn 1.8.0 documentation
1 , 0 ], ... [ - 1 , - 1 , 2 ], ... [ 1 , 1 , 1 ], ......>>> X = np . array ([[ - 1 , - 1 , - 1 ], [ 0 , 0 , 3 ]]) >>> dictionary...scikit-learn.org/stable/modules/generated/sklearn.decomposition.SparseCoder.html -
CategoricalNB — scikit-learn 1.8.0 documentation
Added in version 1.2. Changed in version 1.4: The default value... CategoricalNB ( * , alpha = 1.0 , force_alpha = True , fit_prior...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.CategoricalNB.html -
LabelEncoder — scikit-learn 1.8.0 documentation
classes_ array([1, 2, 6]) >>> le . transform ([ 1 , 1 , 2 , 6 ]) array([0,...array([0, 0, 1, 2]...) >>> le . inverse_transform ([ 0 , 0 , 1 , 2 ])...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html -
Normalizer — scikit-learn 1.8.0 documentation
1 , 2 , 2 ], ... [ 1 , 3 , 9 , 3 ], ... [...0.4, 0.4], [0.1, 0.3, 0.9, 0.3], [0.5, 0.7, 0.5, 0.1]]) fit ( X...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html -
OrdinalEncoder — scikit-learn 1.8.0 documentation
inverse_transform ([[ 1 , 0 ], [ 0 , 1 ]]) array([['Male', 1], ['Female',... =- 1 ) . fit_transform ( X ) array([[ 1., 0.], [ 0., 1.], [...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html