From bb29ce2818967481f666e219254a5ec87081f523 Mon Sep 17 00:00:00 2001 From: Jiaming Yuan Date: Fri, 17 Apr 2020 03:08:55 +0800 Subject: [PATCH] Add missing aft parameters. [skip ci] (#5553) --- doc/parameter.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/doc/parameter.rst b/doc/parameter.rst index 606aa29c1..a7c0479ae 100644 --- a/doc/parameter.rst +++ b/doc/parameter.rst @@ -351,6 +351,9 @@ Specify the learning task and the corresponding learning objective. The objectiv - ``survival:cox``: Cox regression for right censored survival time data (negative values are considered right censored). Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional hazard function ``h(t) = h0(t) * HR``). + - ``survival:aft``: Accelerated failure time model for censored survival time data. + See :doc:`/tutorials/aft_survival_analysis` for details. + - ``aft_loss_distribution``: Probabilty Density Function used by ``survival:aft`` and ``aft-nloglik`` metric. - ``multi:softmax``: set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes) - ``multi:softprob``: same as softmax, but output a vector of ``ndata * nclass``, which can be further reshaped to ``ndata * nclass`` matrix. The result contains predicted probability of each data point belonging to each class. - ``rank:pairwise``: Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized @@ -389,6 +392,8 @@ Specify the learning task and the corresponding learning objective. The objectiv - ``cox-nloglik``: negative partial log-likelihood for Cox proportional hazards regression - ``gamma-deviance``: residual deviance for gamma regression - ``tweedie-nloglik``: negative log-likelihood for Tweedie regression (at a specified value of the ``tweedie_variance_power`` parameter) + - ``aft-nloglik``: Negative log likelihood of Accelerated Failure Time model. + See :doc:`/tutorials/aft_survival_analysis` for details. * ``seed`` [default=0]