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Fix aft_loss_distribution documentation (#8995)

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Rong Ou 2023-03-29 19:13:23 -07:00 committed by GitHub
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doc/parameter.rst
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@ -372,7 +372,6 @@ Specify the learning task and the corresponding learning objective. The objectiv
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``: Probability Density Function used by ``survival:aft`` objective 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
@ -468,6 +467,11 @@ Parameter for using Quantile Loss (``reg:quantileerror``)
* ``quantile_alpha``: A scala or a list of targeted quantiles.
Parameter for using AFT Survival Loss (``survival:aft``) and Negative Log Likelihood of AFT metric (``aft-nloglik``)
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* ``aft_loss_distribution``: Probability Density Function, ``normal``, ``logistic``, or ``extreme``.
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Command Line Parameters
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