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grammar fixes and typos (#3568)

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Zeno Gantner 2018-08-08 18:48:27 +02:00 committed by Philip Hyunsu Cho
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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 Most of parameters in XGBoost are about bias variance tradeoff. The best model
should trade the model complexity with its predictive power carefully. should trade the model complexity with its predictive power carefully.
:doc:`Parameters Documentation </parameter>` will tell you whether each parameter :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. turn the knob between complicated model and simple model.
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@ -27,16 +27,16 @@ Control Overfitting
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When you observe high training accuracy, but low test accuracy, it is likely that you encountered overfitting problem. 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``. - This includes ``subsample`` and ``colsample_bytree``.
- You can also reduce stepsize ``eta``. Rremember to increase ``num_round`` when you do so. - You can also reduce stepsize ``eta``. Remember to increase ``num_round`` when you do so.
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Handle Imbalanced Dataset Handle Imbalanced Dataset