* Revert "Fix #3485, #3540: Don't use dropout for predicting test sets (#3556)"
This reverts commit 44811f233071c5805d70c287abd22b155b732727.
* Document behavior of predict() for DART booster
* Add notice to parameter.rst
* Fix bug of using list(x) function when x is string
list('abcdcba') = ['a', 'b', 'c', 'd', 'c', 'b', 'a']
* Allow feature_names/feature_types to be of any type
If feature_names/feature_types is iterable, e.g. tuple, list, then convert the value to list, except for string; otherwise construct a list with a single value
* Delete excess whitespace
* Fix whitespace to pass lint
Add `'total_gain'` and `'total_cover'` as possible `importance_type`
arguments to `Booster.get_score` in the Python package.
`get_score` already accepts a `'gain'` argument, which returns each
feature's average gain over all of its splits. `'total_gain'` does the
same, but returns a total rather than an average. This seems more
intuitively meaningful, and also matches the behavior of the R package's
`xgb.importance` function.
I also added an analogous `'total_cover'` command for consistency.
This should resolve#3484.
* Improved library loading a bit
* Fixed indentation.
* Fixes according to the discussion
* Moved the comment to a separate line.
* specified exception type
* allow arbitrary cross validation fold indices
- use training indices passed to `folds` parameter in `training.cv`
- update doc string
* add tests for arbitrary fold indices
* Add option to use weights when evaluating metrics in validation sets
* Add test for validation-set weights functionality
* simplify case with no weights for test sets
* fix lint issues
* Now `make pippack` works without any manual action: it will produce
xgboost-[version].tar.gz, which one can use by typing
`pip3 install xgboost-[version].tar.gz`.
* Detect OpenMP-capable compilers (clang, gcc-5, gcc-7) on MacOS
* Support CSV file in DMatrix
We'd just need to expose the CSV parser in dmlc-core to the Python wrapper
* Revert extra code; document existing CSV support
CSV support is already there but undocumented
* Add notice about categorical features
* Add interaction effects and cox loss
* Minimize whitespace changes
* Cox loss now no longer needs a pre-sorted dataset.
* Address code review comments
* Remove mem check, rename to pred_interactions, include bias
* Make lint happy
* More lint fixes
* Fix cox loss indexing
* Fix main effects and tests
* Fix lint
* Use half interaction values on the off-diagonals
* Fix lint again
After installing ``gcc@5``, ``CMAKE_C_COMPILER`` will not be set to gcc-5 in some macOS environment automatically and the installation of xgboost will still fail. Manually setting the compiler will solve the problem.
I found the installation of the Python XGBoost package to be problematic as the documentation around compiler requirements was unclear, as discussed in #1501. I decided that I would improve the README.
* SHAP values for feature contributions
* Fix commenting error
* New polynomial time SHAP value estimation algorithm
* Update API to support SHAP values
* Fix merge conflicts with updates in master
* Correct submodule hashes
* Fix variable sized stack allocation
* Make lint happy
* Add docs
* Fix typo
* Adjust tolerances
* Remove unneeded def
* Fixed cpp test setup
* Updated R API and cleaned up
* Fixed test typo
* repared serialization after update process; fixes#2545
* non-stratified folds in python could omit some data instances
* Makefile: fixes for older makes on windows; clean R-package too
* make cub to be a shallow submodule
* improve $(MAKE) recovery
