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[Doc] Clarify the output behavior of reg:logistic (#9435)

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Philip Hyunsu Cho 2023-08-03 20:42:07 -07:00 committed by GitHub
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doc/parameter.rst
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@ -345,7 +345,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
- ``reg:squarederror``: regression with squared loss. - ``reg:squarederror``: regression with squared loss.
- ``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: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:logistic``: logistic regression, output probability
- ``reg:pseudohubererror``: regression with Pseudo Huber loss, a twice differentiable alternative to absolute loss. - ``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. If used in distributed training, the leaf value is calculated as the mean value from all workers, which is not guaranteed to be optimal. - ``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.