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Merge pull request #860 from zhengruifeng/mae

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Add "mean absolute error" to metrics
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Tianqi Chen
2016-02-25 12:17:03 -08:00
parent 4c40fdb73a 10af94c77e
commit bb0d163d22
2 changed files with 14 additions and 0 deletions
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doc/parameter.md
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@@ -80,6 +80,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
- User can add multiple evaluation metrics, for python user, remember to pass the metrics in as list of parameters pairs instead of map, so that latter 'eval_metric' won't override previous one
- The choices are listed below:
- "rmse": [root mean square error](http://en.wikipedia.org/wiki/Root_mean_square_error)
- "mae": [mean absolute error](https://en.wikipedia.org/wiki/Mean_absolute_error)
- "logloss": negative [log-likelihood](http://en.wikipedia.org/wiki/Log-likelihood)
- "error": Binary classification error rate. It is calculated as #(wrong cases)/#(all cases). For the predictions, the evaluation will regard the instances with prediction value larger than 0.5 as positive instances, and the others as negative instances.
- "merror": Multiclass classification error rate. It is calculated as #(wrong cases)/#(all cases).