GitLab

http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html

* Add Huber loss

https://en.wikipedia.org/wiki/Huber_loss

* update

* update docs/Parameters.md

* update

* use handle is not None for _is_constructed

* sort imports; clean code; move FAQ to docs

* fix reset parameter

* redefine CVBooster

* env.model won't be None

* update env.params

* simplify Dataset class

* simplify check output; fix deprecated warning

* format python code with pep8

* **DO NOT MERGE** deliberately break rules to see what will happen during check

* Revert "**DO NOT MERGE** deliberately break rules to see what will happen during check"

This reverts commit 0db93cd7a43c7efa43a2112ada43d46c6f9115d9.

* fix format in test.py

* add docs for pep-8

* add @property to sklearn interface

* add deprecated; fix binary_metric

* support pickle

* add pickle/joblib test; change test_basic to unittest

* remove file for deepcopy

* fix tests

* test basic predict from file

* Revert "test basic predict from file"

This reverts commit 60d2c3158537fd56081f60f1d6d120cedd782887.

* test predict from file

* use tempfile for copy & pickle

* use tempfile w/o binary mode

* clean test

1. add csc support
2. some data type from float to double

* fix creating valid_sets

* support both list and dict

* update Python-API.md

* remove other parameters in sklearn fit

* add dart parameters to sklearn init

* add troubleshooting

* use unittest

* update unittest version

* fix test_engine.py

* fix test_sklearn.py

* default eval_metric by subclass

* add test grid search

* remove verbose_eval

add learning rate to dart (#133)

* add gridsearch example for python sklearn

* reset_learning_rate->reset_parameter

* make callbacks public

* add python doc

* add boosting_type to python sklearn

* fix error

Provide a high level Dataset class for easy use.

current problem:
callback order: user-defined in callback parameter -> reset_learning_rate/print_evaluation/record_evaluation/early_stop
user can't insert a callback between last 4 callbacks

solution:
set order variable:
reset_learning_rate = 10
print_evaluation = 10
record_evaluation = 20
early_stop = 30
user-defined = some int
default: according to index in callback parameter list, = index - len(callbacks) (all < 0)

current callback order:
before iter: user-defined -> reset_learning_rate -> user-defined
after iter: user-defined -> print_evaluation -> user-defined -> record_evaluation -> user-defined -> early_stop -> user-defined

* add feature importances in python; add pandas support

* solve best_iteration issue

Categorical feature support (#108)