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[Doc] fix typos in documentation (#9458)

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This commit is contained in:
James Lamb
2023-08-10 06:26:36 -05:00
committed by GitHub
parent 4359356d46
commit 9dbb71490c
18 changed files with 32 additions and 31 deletions
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6
doc/tutorials/custom_metric_obj.rst
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@@ -38,7 +38,7 @@ Although XGBoost has native support for said functions, using it for demonstrati
provides us the opportunity of comparing the result from our own implementation and the
one from XGBoost internal for learning purposes. After finishing this tutorial, we should
be able to provide our own functions for rapid experiments. And at the end, we will
provide some notes on non-identy link function along with examples of using custom metric
provide some notes on non-identity link function along with examples of using custom metric
and objective with the `scikit-learn` interface.
If we compute the gradient of said objective function:
@@ -165,7 +165,7 @@ Reverse Link Function
When using builtin objective, the raw prediction is transformed according to the objective
function. When a custom objective is provided XGBoost doesn't know its link function so the
user is responsible for making the transformation for both objective and custom evaluation
metric. For objective with identiy link like ``squared error`` this is trivial, but for
metric. For objective with identity link like ``squared error`` this is trivial, but for
other link functions like log link or inverse link the difference is significant.
For the Python package, the behaviour of prediction can be controlled by the
@@ -173,7 +173,7 @@ For the Python package, the behaviour of prediction can be controlled by the
parameter without a custom objective, the metric function will receive transformed
prediction since the objective is defined by XGBoost. However, when the custom objective is
also provided along with that metric, then both the objective and custom metric will
recieve raw prediction. The following example provides a comparison between two different
receive raw prediction. The following example provides a comparison between two different
behavior with a multi-class classification model. Firstly we define 2 different Python
metric functions implementing the same underlying metric for comparison,
`merror_with_transform` is used when custom objective is also used, otherwise the simpler