scikit-learn / scikit-learn
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        nodes to go from the root to the deepest leaf. Must be strictly greater
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        than 1. Depth isn't constrained by default.
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    min_samples_leaf : int, optional (default=20)
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        The minimum number of samples per leaf.
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        The minimum number of samples per leaf. For small datasets with less
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        than a few hundred samples, it is recommended to lower this value since
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        only very shallow trees would be built.
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    l2_regularization : float, optional (default=0)
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        The L2 regularization parameter. Use ``0`` for no regularization
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        (default).
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        nodes to go from the root to the deepest leaf. Must be strictly greater
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        than 1. Depth isn't constrained by default.
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    min_samples_leaf : int, optional (default=20)
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        The minimum number of samples per leaf.
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        The minimum number of samples per leaf. For small datasets with less
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        than a few hundred samples, it is recommended to lower this value since
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        only very shallow trees would be built.
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    l2_regularization : float, optional (default=0)
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        The L2 regularization parameter. Use 0 for no regularization.
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    max_bins : int, optional (default=256)
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sklearn 95.68%
Project Totals (383 files) 95.68%
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coverage:
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  status:
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    project:
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      default:
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        # Commits pushed to master should not make the overall
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        # project coverage decrease by more than 1%:
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        target: auto
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        threshold: 1%
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      default:
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        # Be tolerant on slight code coverage diff on PRs to limit
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        # noisy red coverage status on github PRs.
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        # Note The coverage stats are still uploaded
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        # to codecov so that PR reviewers can see uncovered lines
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        # in the github diff if they install the codecov browser
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        # extension:
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        # https://github.com/codecov/browser-extension
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        target: auto
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        threshold: 1%
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ignore:
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- "sklearn/externals"
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