Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Edit grammar in custom metric tutorial (#8163)

Browse Source
This commit is contained in:
Ravi Makhija 2022-08-12 13:02:25 -04:00 committed by GitHub
parent f421c26d35
commit fa869eebd9
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 8 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
doc/tutorials/custom_metric_obj.rst
View File

@ -24,7 +24,7 @@ concepts should be readily applicable to other language bindings.
* Breaking change was made in XGBoost 1.6. * Breaking change was made in XGBoost 1.6.
In the following two sections, we will provide a step by step walk through of implementing In the following two sections, we will provide a step by step walk through of implementing
``Squared Log Error(SLE)`` objective function: the ``Squared Log Error (SLE)`` objective function:
.. math:: .. math::
\frac{1}{2}[log(pred + 1) - log(label + 1)]^2 \frac{1}{2}[log(pred + 1) - log(label + 1)]^2
@ -114,10 +114,10 @@ monitor our model's performance. As mentioned above, the default metric for ``S
elements = np.power(np.log1p(y) - np.log1p(predt), 2) elements = np.power(np.log1p(y) - np.log1p(predt), 2)
return 'PyRMSLE', float(np.sqrt(np.sum(elements) / len(y))) return 'PyRMSLE', float(np.sqrt(np.sum(elements) / len(y)))
Since we are demonstrating in Python, the metric or objective needs not be a function, Since we are demonstrating in Python, the metric or objective need not be a function,
any callable object should suffice. Similarly to the objective function, our metric also any callable object should suffice. Similar to the objective function, our metric also
accepts ``predt`` and ``dtrain`` as inputs, but returns the name of metric itself and a accepts ``predt`` and ``dtrain`` as inputs, but returns the name of the metric itself and a
floating point value as result. After passing it into XGBoost as argument of ``feval`` floating point value as the result. After passing it into XGBoost as argument of ``feval``
parameter: parameter:
.. code-block:: python .. code-block:: python
@ -154,7 +154,7 @@ Reverse Link Function
********************* *********************
When using builtin objective, the raw prediction is transformed according to the objective When using builtin objective, the raw prediction is transformed according to the objective
function. When custom objective is provided XGBoost doesn't know its link function so the 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 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 identiy link like ``squared error`` this is trivial, but for
other link functions like log link or inverse link the difference is significant. other link functions like log link or inverse link the difference is significant.
@ -162,9 +162,9 @@ 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 For the Python package, the behaviour of prediction can be controlled by the
``output_margin`` parameter in ``predict`` function. When using the ``custom_metric`` ``output_margin`` parameter in ``predict`` function. When using the ``custom_metric``
parameter without a custom objective, the metric function will receive transformed parameter without a custom objective, the metric function will receive transformed
prediction since the objective is defined by XGBoost. However, when custom objective is 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 also provided along with that metric, then both the objective and custom metric will
recieve raw prediction. Following example provides a comparison between two different recieve raw prediction. The following example provides a comparison between two different
behavior with a multi-class classification model. Firstly we define 2 different Python behavior with a multi-class classification model. Firstly we define 2 different Python
metric functions implementing the same underlying metric for comparison, metric functions implementing the same underlying metric for comparison,
`merror_with_transform` is used when custom objective is also used, otherwise the simpler `merror_with_transform` is used when custom objective is also used, otherwise the simpler