API Reference

References/Python/scikit-learn/API Reference/grid_search

Warning DEPRECATED

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References/Python/scikit-learn/API Reference/svm

class sklearn.svm.NuSVC(nu=0.5, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None

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covariance.MinCovDet()

References/Python/scikit-learn/API Reference/covariance

class sklearn.covariance.MinCovDet(store_precision=True, assume_centered=False, support_fraction=None, random_state=None)

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cross_validation.LeaveOneOut()

References/Python/scikit-learn/API Reference/cross_validation

Warning DEPRECATED

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sklearn.svm.libsvm.decision_function()

References/Python/scikit-learn/API Reference/svm

sklearn.svm.libsvm.decision_function() Predict margin (libsvm name for this is predict_values) We

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preprocessing.Normalizer()

References/Python/scikit-learn/API Reference/preprocessing

class sklearn.preprocessing.Normalizer(norm='l2', copy=True)

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gaussian_process.kernels.RationalQuadratic()

References/Python/scikit-learn/API Reference/gaussian_process

class sklearn.gaussian_process.kernels.RationalQuadratic(length_scale=1.0, alpha=1.0, length_scale_bounds=(1e-05

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tree.DecisionTreeClassifier()

References/Python/scikit-learn/API Reference/tree

class sklearn.tree.DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1

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sklearn.datasets.load_diabetes()

References/Python/scikit-learn/API Reference/datasets

sklearn.datasets.load_diabetes(return_X_y=False)

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sklearn.linear_model.orthogonal_mp()

References/Python/scikit-learn/API Reference/linear_model

sklearn.linear_model.orthogonal_mp(X, y, n_nonzero_coefs=None, tol=None, precompute=False, copy_X=True, return_path=False, r

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