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Merge remote-tracking branch 'ligth_gbm/master' into 3313-enable-auto-early-stopping
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README.md

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@@ -126,6 +126,8 @@ lightgbm-transform (feature transformation binding): https://github.com/microsof
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`postgresml` (LightGBM training and prediction in SQL, via a Postgres extension): https://github.com/postgresml/postgresml
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`vaex-ml` (Python DataFrame library with its own interface to LightGBM): https://github.com/vaexio/vaex
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Support
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-------
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python-package/lightgbm/engine.py

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@@ -658,13 +658,18 @@ def cv(
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Returns
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-------
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eval_hist : dict
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Evaluation history.
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eval_results : dict
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History of evaluation results of each metric.
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The dictionary has the following format:
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{'metric1-mean': [values], 'metric1-stdv': [values],
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'metric2-mean': [values], 'metric2-stdv': [values],
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{'valid metric1-mean': [values], 'valid metric1-stdv': [values],
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'valid metric2-mean': [values], 'valid metric2-stdv': [values],
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...}.
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If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key.
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If ``eval_train_metric=True``, also returns the train metric history.
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In this case, the dictionary has the following format:
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{'train metric1-mean': [values], 'valid metric1-mean': [values],
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'train metric2-mean': [values], 'valid metric2-mean': [values],
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...}.
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"""
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if not isinstance(train_set, Dataset):
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raise TypeError(f"cv() only accepts Dataset object, train_set has type '{type(train_set).__name__}'.")

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