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Add rmsle metric and reg:squaredlogerror objective (#4541)

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Jiaming Yuan
2019-06-11 05:48:27 +08:00
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parent 9683fd433e
commit 2f1319f273
7 changed files with 92 additions and 9 deletions
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doc/parameter.rst
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@@ -151,7 +151,7 @@ Parameters for Tree Booster
- ``refresh``: refreshes tree's statistics and/or leaf values based on the current data. Note that no random subsampling of data rows is performed.
- ``prune``: prunes the splits where loss < min_split_loss (or gamma).
- In a distributed setting, the implicit updater sequence value would be adjusted to ``grow_histmaker,prune`` by default, and you can set ``tree_method`` as ``hist`` to use ``grow_histmaker``.
- In a distributed setting, the implicit updater sequence value would be adjusted to ``grow_histmaker,prune`` by default, and you can set ``tree_method`` as ``hist`` to use ``grow_histmaker``.
* ``refresh_leaf`` [default=1]
@@ -295,6 +295,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
* ``objective`` [default=reg:squarederror]
- ``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
- ``binary:logistic``: logistic regression for binary classification, output probability
- ``binary:logitraw``: logistic regression for binary classification, output score before logistic transformation
@@ -325,6 +326,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
- The choices are listed below:
- ``rmse``: `root mean square error <http://en.wikipedia.org/wiki/Root_mean_square_error>`_
- ``rmsle``: root mean square log error: :math:`\sqrt{\frac{1}{N}[log(pred + 1) - log(label + 1)]^2}`. Default metric of ``reg:squaredlogerror`` objective. This metric reduces errors generated by outliers in dataset. But because ``log`` function is employed, ``rmsle`` might output ``nan`` when prediction value is less than -1. See ``reg:squaredlogerror`` for other requirements.
- ``mae``: `mean absolute error <https://en.wikipedia.org/wiki/Mean_absolute_error>`_
- ``logloss``: `negative log-likelihood <http://en.wikipedia.org/wiki/Log-likelihood>`_
- ``error``: Binary classification error rate. It is calculated as ``#(wrong cases)/#(all cases)``. For the predictions, the evaluation will regard the instances with prediction value larger than 0.5 as positive instances, and the others as negative instances.