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Backport doc fixes that are compatible with 0.72 release

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* Clarify behavior of LIBSVM in XGBoost4J-Spark (#3524)
* Fix typo in faq.rst (#3521)
* Fix typo in parameter.rst, gblinear section (#3518)
* Clarify supported OSes for XGBoost4J published JARs (#3547)
* Update broken links (#3565)
* Grammar fixes and typos (#3568)
* Bring XGBoost4J Intro up-to-date (#3574)
This commit is contained in:
Nan Zhu
2018-07-28 17:34:39 -07:00
committed by Philip Cho
parent e19dded9a3
commit 4334b9cc91
6 changed files with 77 additions and 80 deletions
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14
doc/tutorials/param_tuning.rst
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@@ -19,7 +19,7 @@ However, such complicated model requires more data to fit.
Most of parameters in XGBoost are about bias variance tradeoff. The best model
should trade the model complexity with its predictive power carefully.
:doc:`Parameters Documentation </parameter>` will tell you whether each parameter
ill make the model more conservative or not. This can be used to help you
will make the model more conservative or not. This can be used to help you
turn the knob between complicated model and simple model.
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@@ -27,16 +27,16 @@ Control Overfitting
*******************
When you observe high training accuracy, but low test accuracy, it is likely that you encountered overfitting problem.
There are in general two ways that you can control overfitting in XGBoost
There are in general two ways that you can control overfitting in XGBoost:
* The first way is to directly control model complexity
* The first way is to directly control model complexity.
- This include ``max_depth``, ``min_child_weight`` and ``gamma``
- This includes ``max_depth``, ``min_child_weight`` and ``gamma``.
* The second way is to add randomness to make training robust to noise
* The second way is to add randomness to make training robust to noise.
- This include ``subsample`` and ``colsample_bytree``.
- You can also reduce stepsize ``eta``. Rremember to increase ``num_round`` when you do so.
- This includes ``subsample`` and ``colsample_bytree``.
- You can also reduce stepsize ``eta``. Remember to increase ``num_round`` when you do so.
*************************
Handle Imbalanced Dataset