From 1aabc690ec3969c3e62666cd702808d870e8671c Mon Sep 17 00:00:00 2001 From: Philip Hyunsu Cho Date: Thu, 3 Aug 2023 20:42:07 -0700 Subject: [PATCH] [Doc] Clarify the output behavior of reg:logistic (#9435) --- doc/parameter.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/parameter.rst b/doc/parameter.rst index 6f767c80d..1b1bb80a4 100644 --- a/doc/parameter.rst +++ b/doc/parameter.rst @@ -345,7 +345,7 @@ Specify the learning task and the corresponding learning objective. The objectiv - ``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:logistic``: logistic regression. + - ``reg:logistic``: logistic regression, output probability - ``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.