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FunctionTransformer — scikit-learn 1.6.1 docume...
Added in version 1.1. kw_args dict, default=None Dictionary...array([[0. , 0.6931...], [1.0986..., 1.3862...]]) fit ( X , y =...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html -
GaussianProcessRegressor — scikit-learn 1.6.1 d...
ConstantKernel(1.0, constant_value_bounds="fixed") * RBF(1.0, lengt...implementation is based on Algorithm 2.1 of [RW2006] . In addition to standard...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html -
f_regression — scikit-learn 1.6.1 documentation
r_regression values lie in [-1, 1] and can thus be negative. f_regression...set to 0.0 . Added in version 1.1. Returns : f_statistic ndarray...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html -
TargetEncoder — scikit-learn 1.6.1 documentation
1 ] * 15 + [ 20.4 ] * 5 + [ 20.1 ] * 25 + [ 21.2...scikit-learn 1.3 Release Highlights for scikit-learn 1.3 Comparing...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html -
adjusted_rand_score — scikit-learn 1.6.1 docume...
1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> adjusted_rand_score...adjusted_rand_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Labelings that...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.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 -
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 -
Kernel — scikit-learn 1.6.1 documentation
length_scale = 1.0 ): ... self . length_scale =...2.0 ) >>> X = np . array ([[ 1 , 2 ], [ 3 , 4 ]]) >>> print (...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Kernel.html -
Ridge — scikit-learn 1.6.1 documentation
Ridge ( alpha = 1.0 , * , fit_intercept = True ,...shape (n_targets,)}, default=1.0 Constant that multiplies the...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html