* Added NaN support in mapper
* pep
* WIP
* some more
* WIP
* WIP
* bug fix
* basic tests
* some doc
* avoid some interactions
* Added tag
* better test
* decent test + fix bug
* add missing_fraction param to benchmark
* bin training and validation data separately
* shorter test
* Map missing values to first bin instead of last
* pep8
* Added whats new entry
* avoid some python interactions
* make predict_binned work
* fixed bug due to offset in bin_thresholds_ attribute
* more sensible binning strat
* typo
* user name
* Add small test
* convert to fortran array in tests
* some doc
* Added function test
* pep8
* Bin validation data using binmaper of training data
* Allocate first bin for missing entries based on the whole data, not just
training data.
* Addressed Thomas' comments
* Update sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py
* Addressed Guillaume's comments
* always allocate first bin for missing values
* reduce diff
* minor more consistent test
* typo
* WIP
* some doc
* reduce diff
* pep8
* minor
* remove prints
* towards nan only splits
* don't check right to left on split_on_nan
* cleaups
* format and comment
* Fixed bug + added more tests
* refactor tests
* put back n_threads to max value
* minor changes
* minor cleaning
* Add (failing) test that checks equivalence with min max imputation
* Decrease the likelihood of ties when training the trees
* More robust test
* Fix pytest parametrization
* Check bin thresholds in test
* Try to make the test even easier to see if the Linux 32bit build would pass in this case
* Don't check last non-missing bin if there's no nan
* Improve min-max imputation test
* FIX: _find_best_bin_to_split_right_to_left is still required even when left to right wants to split on nans
* comments
* remove split_on_nan
* ooops deleted useless files
* Got rid of individual checks in predictor code
+inf thresholds are only allowed in a split on nan situation.
Thresholds that are computed as +inf are capped to a very high constant
value
* can also remove special case in binning code
* minor typos + more consistent test
* renamed types -> common
* 1e300 -> almost inf
* added user guide section on missing values
* Addressed Olivier's comment + updated whatsnew
* addressed comments
* Fix doctest formatting
* Fix nan predictive doctest
* Change default solver in LogisticRegression
* This is an API change, not a feature
* Decrease numerical precision in LogisticRegression doctest
* ENH add multi_class='auto' for LR, default from 0.22
* No warning when binary
Brian Sun