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GaussianProcessRegressor — scikit-learn 1.5.2 d...
float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...implementation is based on Algorithm 2.1 of [RW2006] . In addition to...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html -
ledoit_wolf — scikit-learn 1.5.2 documentation
2 ], [ .2 , .8 ]]) >>> rng = np . random...scikit-learn.org/stable/modules/generated/sklearn.covariance.ledoit_wolf.html -
SGDOneClassSVM — scikit-learn 1.5.2 documentation
[ - 2 , - 1 ], [ 1 , 1 ], [ 2 , 1 ]]) >>> clf =...scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html -
PolynomialCountSketch — scikit-learn 1.5.2 docu...
degree = 2 , coef0 = 0 , n_components = 100...approximated. degree int, default=2 Degree of the polynomial kernel...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.html -
AffinityPropagation — scikit-learn 1.5.2 docume...
2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2 ], [ 4 , 4...cluster_centers_ array([[1, 2], [4, 2]]) fit ( X , y = None ) [source]...scikit-learn.org/stable/modules/generated/sklearn.cluster.AffinityPropagation.html -
FeatureUnion — scikit-learn 1.5.2 documentation
n_components = 2 ))]) >>> X = [[ 0. , 1. , 3 ], [ 2. , 2. , 5 ]] >>>...parameters. Added in version 1.2. n_features_in_ int Number of...scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html -
OutputCodeClassifier — scikit-learn 1.5.2 docum...
Artificial Intelligence Research 2, 1995. [ 2 ] “The error coding method...n_features = 4 , ... n_informative = 2 , n_redundant = 0 , ... random_state...scikit-learn.org/stable/modules/generated/sklearn.multiclass.OutputCodeClassifier.html -
homogeneity_score — scikit-learn 1.5.2 document...
2 ])) 1.000000 >>> print ( " %.6f...([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 2 , 3 ])) 1.000000 Clusters that...scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_score.html -
ClassifierMixin — scikit-learn 1.5.2 documentation
2 ], [ 2 , 3 ], [ 3 , 4 ]]) >>> y =...scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html -
MinMaxScaler — scikit-learn 1.5.2 documentation
transform ([[ 2 , 2 ]])) [[1.5 0. ]] fit ( X , y...MinMaxScaler >>> data = [[ - 1 , 2 ], [ - 0.5 , 6 ], [ 0 , 10 ],...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html