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

class sklearn.gaussian_process.kernels.DotProduct(sigma_0=1.0, sigma_0_bounds=(1e-05, 100000.0))

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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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sklearn.utils.resample()
  • References/Python/scikit-learn/API Reference/utils

sklearn.utils.resample(*arrays, **options)

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sklearn.decomposition.dict_learning()
  • References/Python/scikit-learn/API Reference/decomposition

sklearn.decomposition.dict_learning(X, n_components, alpha, max_iter=100, tol=1e-08, method='lars', n_jobs=1, dict_init=None

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

sklearn.metrics.mean_absolute_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average')

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

Warning DEPRECATED

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ensemble.VotingClassifier()
  • References/Python/scikit-learn/API Reference/ensemble

class sklearn.ensemble.VotingClassifier(estimators, voting='hard', weights=None, n_jobs=1)

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Comparing various online solvers
  • References/Python/scikit-learn/Examples/Generalized Linear Models

An example showing how different online solvers perform on the hand-written digits dataset.

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exceptions.EfficiencyWarning
  • References/Python/scikit-learn/API Reference/exceptions

class sklearn.exceptions.EfficiencyWarning

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