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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 -
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 -
Hyperparameter — scikit-learn 1.6.1 documentation
n_elements = 1 , fixed = None ) [source] # A...the parameter. If n_elements>1, a pair of 1d array with n_elements...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Hyperparameter.html -
RandomForestClassifier — scikit-learn 1.6.1 doc...
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead...instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The “balanced” mode uses...scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html -
SGDOneClassSVM — scikit-learn 1.6.1 documentation
array ([[ - 1 , - 1 ], [ - 2 , - 1 ], [ 1 , 1 ], [ 2 , 1 ]]) >>>...labels for X. Returns -1 for outliers and 1 for inliers. Parameters...scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html -
RFECV — scikit-learn 1.6.1 documentation
ranking_ array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5]) property...estimator , * , step = 1 , min_features_to_select = 1 , cv = None , scoring...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html -
pairwise_distances — scikit-learn 1.6.1 documen...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...deprecated from SciPy 1.9 and will be removed in SciPy 1.11. Note 'matching'...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html -
LinearDiscriminantAnalysis — scikit-learn 1.6.1...
([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2...2 , 1 ], [ 3 , 2 ]]) >>> y = np . array ([ 1 , 1 , 1 , 2 , 2...scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysi... -
lasso_path — scikit-learn 1.6.1 documentation
alpha * || w || _1 For multi-output tasks it is: ( 1 / ( 2 * n_samples...lasso_path >>> X = np . array ([[ 1 , 2 , 3.1 ], [ 2.3 , 5.4 , 4.3 ]])...scikit-learn.org/stable/modules/generated/sklearn.linear_model.lasso_path.html -
SpectralClustering — scikit-learn 1.6.1 documen...
array ([[ 1 , 1 ], [ 2 , 1 ], [ 1 , 0 ], ... [ 4 , 7...clustering . labels_ array([1, 1, 1, 0, 0, 0]) >>> clustering ...scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html