Commit Graph
7 Commits
Author SHA1 Message Date
Johan Manders 122ec48a89 Update evals_result.py 2015-10-14 13:40:20 +02:00
Johan Manders 6e2bdcbbbc Demo for accessing eval metrics in xgboost 2015-10-14 13:22:39 +02:00
Johan Manders 67f3c687b8 Added Johan Manders to the list, asked by Tianqi Chen 2015-10-14 13:06:14 +02:00
Johan Manders 9c8420a4dc Updated the documentation a bit
Will upload some demos for guide-python later.
2015-10-14 12:53:42 +02:00
Johan Manders e960a09ff4 Made eval_results for sklearn output the same structure as in the new training.py
Changed the name of eval_results to evals_result, so that the naming is the same in training.py and sklearn.py

Made the structure of evals_result the same as in training.py, the names of the keys are different:

In sklearn.py you cannot name your evals_result, but they are automatically called 'validation_0', 'validation_1' etc.
The dict evals_result will output something like: {'validation_0': {'logloss': ['0.674800', '0.657121']}, 'validation_1': {'logloss': ['0.63776', '0.58372']}}

In training.py you can name your multiple evals_result with a watchlist like: watchlist  = [(dtest,'eval'), (dtrain,'train')]
The dict evals_result will output something like: {'train': {'logloss': ['0.68495', '0.67691']}, 'eval': {'logloss': ['0.684877', '0.676767']}}

You can access the evals_result using the evals_result() function.
2015-10-14 12:51:46 +02:00
Johan Manders e339cdec52 Too many branches and unused key 2015-10-12 16:47:24 +02:00
Johan Manders 40566cdbba update sklearn.py because evals_result in training.py changed
Because I changed the training.py, the sklearn.py had to be changed also to be able to read all the data form evals_result.
2015-10-12 16:31:23 +02:00