preprocessing.FunctionTransformer()
  • References/Python/scikit-learn/API Reference/preprocessing

class sklearn.preprocessing.FunctionTransformer(func=None, inverse_func=None, validate=True, accept_sparse=False, pass_y=False

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base.ClusterMixin
  • References/Python/scikit-learn/API Reference/base

class sklearn.base.ClusterMixin

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cross_validation.LabelShuffleSplit()
  • References/Python/scikit-learn/API Reference/cross_validation

Warning DEPRECATED

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feature_selection.SelectFpr()
  • References/Python/scikit-learn/API Reference/feature_selection

class sklearn.feature_selection.SelectFpr(score_func=, alpha=0.05)

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sklearn.isotonic.isotonic_regression()
  • References/Python/scikit-learn/API Reference/isotonic

sklearn.isotonic.isotonic_regression(y, sample_weight=None, y_min=None, y_max=None, increasing=True)

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sklearn.metrics.get_scorer()
  • References/Python/scikit-learn/API Reference/metrics

sklearn.metrics.get_scorer(scoring)

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sklearn.preprocessing.add_dummy_feature()
  • References/Python/scikit-learn/API Reference/preprocessing

sklearn.preprocessing.add_dummy_feature(X, value=1.0)

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feature_selection.RFE()
  • References/Python/scikit-learn/API Reference/feature_selection

class sklearn.feature_selection.RFE(estimator, n_features_to_select=None, step=1, verbose=0)

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gaussian_process.kernels.ExpSineSquared()
  • References/Python/scikit-learn/API Reference/gaussian_process

class sklearn.gaussian_process.kernels.ExpSineSquared(length_scale=1.0, periodicity=1.0, length_scale_bounds=(1e-05

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sklearn.svm.libsvm.predict()
  • References/Python/scikit-learn/API Reference/svm

sklearn.svm.libsvm.predict() Predict target values of X given a model (low-level method)

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