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[pyspark] Use quantile dmatrix. (#8284)

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Jiaming Yuan
2022-10-12 20:38:53 +08:00
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commit 97a5b088a5
9 changed files with 225 additions and 120 deletions
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doc/parameter.rst
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@@ -349,7 +349,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
- ``reg:squaredlogerror``: regression with squared log loss :math:`\frac{1}{2}[log(pred + 1) - log(label + 1)]^2`. All input labels are required to be greater than -1. Also, see metric ``rmsle`` for possible issue with this objective.
- ``reg:logistic``: logistic regression.
- ``reg:pseudohubererror``: regression with Pseudo Huber loss, a twice differentiable alternative to absolute loss.
- ``reg:absoluteerror``: Regression with L1 error. When tree model is used, leaf value is refreshed after tree construction.
- ``reg:absoluteerror``: Regression with L1 error. When tree model is used, leaf value is refreshed after tree construction. If used in distributed training, the leaf value is calculated as the mean value from all workers, which is not guaranteed to be optimal.
- ``binary:logistic``: logistic regression for binary classification, output probability
- ``binary:logitraw``: logistic regression for binary classification, output score before logistic transformation
- ``binary:hinge``: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.