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Suggestion: Add training_data metrics in cv return #1105
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This would be a nice feature to have, it'll help monitor whether overfit occurred judging from cv. |
It will be a very useful feature, I hope it will be implemented as soon as possible. |
Closed via #2089. |
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Environment info
Operating System: WIN10
CPU:
C++/Python/R version: python3.6
a suggestion :
Add training_data performance(metrics) in cv return for drawing validation curve (just as Xgboost.cv's return) since skleran.validation_curve is very inefficient (run n time , n =number of iteration). Don't know if anyone else has this need though.
Example from return of Xgboost.cv (after` pandas.from_dict):
Out[23]:
test-auc-mean test-auc-std train-auc-mean train-auc-std
0 0.847553 0.011909 0.849978 0.000601
1 0.852323 0.012572 0.856189 0.000791
2 0.856945 0.011464 0.863983 0.000770
3 0.857423 0.011400 0.869057 0.000768
4 0.857955 0.010694 0.872355 0.000579
5 0.858113 0.011444 0.874590 0.000896
6 0.857701 0.011529 0.876404 0.000870
`
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