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Merge pull request #728 from yenchenlin1994/fix-doc-typo

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Remove redundant word
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Yuan (Terry) Tang 2016-01-07 08:25:31 -06:00
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@ -162,7 +162,7 @@ After we remove all the constants, the specific objective at step ``$t$`` become
This becomes our optimization goal for the new tree. One important advantage of this definition is that
it only depends on ``$g_i$`` and ``$h_i$``. This is how xgboost can support custom loss functions.
We can optimize every loss function, including logistic regression, weighted logistic regression, using the exactly
We can optimize every loss function, including logistic regression, weighted logistic regression, using exactly
the same solver that takes ``$g_i$`` and ``$h_i$`` as input!
### Model Complexity