tree.ExtraTreeRegressor()
  • References/Python/scikit-learn/API Reference/tree

class sklearn.tree.ExtraTreeRegressor(criterion='mse', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1,

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Recursive feature elimination
  • References/Python/scikit-learn/Examples/Feature Selection

A recursive feature elimination example showing the relevance of pixels in a digit classification task.

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cluster.Birch()
  • References/Python/scikit-learn/API Reference/cluster

class sklearn.cluster.Birch(threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True)

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sklearn.datasets.fetch_olivetti_faces()
  • References/Python/scikit-learn/API Reference/datasets

sklearn.datasets.fetch_olivetti_faces(data_home=None, shuffle=False, random_state=0, download_if_missing=True)

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qda.QDA()
  • References/Python/scikit-learn/API Reference/qda

Warning DEPRECATED class sklearn

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sklearn.datasets.load_mlcomp()
  • References/Python/scikit-learn/API Reference/datasets

sklearn.datasets.load_mlcomp(name_or_id, set_='raw', mlcomp_root=None, **kwargs)

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linear_model.LinearRegression()
  • References/Python/scikit-learn/API Reference/linear_model

class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)

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sklearn.base.clone()
  • References/Python/scikit-learn/API Reference/base

sklearn.base.clone(estimator, safe=True)

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

class sklearn.preprocessing.RobustScaler(with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)

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sklearn.learning_curve.validation_curve()
  • References/Python/scikit-learn/API Reference/learning_curve

Warning DEPRECATED

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