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Configuration for init estimation. (#8343)

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* Configuration for init estimation.

* Check whether the model needs configuration based on const attribute `ModelFitted`
instead of a mutable state.
* Add parameter `boost_from_average` to tell whether the user has specified base score.
* Add tests.
This commit is contained in:
Jiaming Yuan
2022-10-18 01:52:24 +08:00
committed by GitHub
parent 2176e511fc
commit 031d66ec27
10 changed files with 247 additions and 111 deletions
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doc/parameter.rst
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@@ -370,9 +370,11 @@ Specify the learning task and the corresponding learning objective. The objectiv
- ``reg:gamma``: gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be `gamma-distributed <https://en.wikipedia.org/wiki/Gamma_distribution#Occurrence_and_applications>`_.
- ``reg:tweedie``: Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be `Tweedie-distributed <https://en.wikipedia.org/wiki/Tweedie_distribution#Occurrence_and_applications>`_.
* ``base_score`` [default=0.5]
* ``base_score``
- The initial prediction score of all instances, global bias
- The parameter is automatically estimated for selected objectives before training. To
disable the estimation, specify a real number argument.
- For sufficient number of iterations, changing this value will not have too much effect.
* ``eval_metric`` [default according to objective]