A new parameter `custom_metric` is added to `train` and `cv` to distinguish the behaviour from the old `feval`. And `feval` is deprecated. The new `custom_metric` receives transformed prediction when the built-in objective is used. This enables XGBoost to use cost functions from other libraries like scikit-learn directly without going through the definition of the link function.
`eval_metric` and `early_stopping_rounds` in sklearn interface are moved from `fit` to `__init__` and is now saved as part of the scikit-learn model. The old ones in `fit` function are now deprecated. The new `eval_metric` in `__init__` has the same new behaviour as `custom_metric`.
Added more detailed documents for the behaviour of custom objective and metric.
* Support more input types for categorical data.
* Shorten the type name from "categorical" to "c".
* Tests for np/cp array and scipy csr/csc/coo.
* Specify the type for feature info.
* Add feature score support for linear model.
* Port R interface to the new implementation.
* Add linear model support in Python.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Change DefaultEvalMetric of classification from error to logloss
* Change default binary metric in plugin/example/custom_obj.cc
* Set old error metric in python tests
* Set old error metric in R tests
* Fix missed eval metrics and typos in R tests
* Fix setting eval_metric twice in R tests
* Add warning for empty eval_metric for classification
* Fix Dask tests
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* Set output margin to True for custom objective in Python and R.
* Add a demo for writing multi-class custom objective function.
* Run tests on selected demos.
* Some minor changes to the code style
Some minor changes to the code style in file basic_walkthrough.py
* coding style changes
* coding style changes arrcording PEP8
* Update basic_walkthrough.py
* Updated sklearn_parallel.py for soon-to-be-deprecated modules
* Updated predict_leaf_indices.py; Use python3 print() as other exmaples and removed unused module
* Fix various typos
* Add override to functions that are overridden
gcc gives warnings about functions that are being overridden by not
being marked as oveirridden. This fixes it.
* Use bst_float consistently
Use bst_float for all the variables that involve weight,
leaf value, gradient, hessian, gain, loss_chg, predictions,
base_margin, feature values.
In some cases, when due to additions and so on the value can
take a larger value, double is used.
This ensures that type conversions are minimal and reduces loss of
precision.
* Add support for Gamma regression
* Use base_score to replace the lp_bias
* Remove the lp_bias config block
* Add a demo for running gamma regression in Python
* Typo fix
* Revise the description for objective
* Add a script to generate the autoclaims dataset
