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AUC in rapidminer results in 0

hii all,
I have a dataset with 2 columns, the first one is label and the second column is prediction i.e. i have stored results of my algorithm in a column and now i want to evaluate performance of that algorithm with rapidminer. i have imported data and specified the columns as label and prediction and evaluating the performance using binomial classification performance evaluation. it gives correct results on confusion matrix and f-measure but in AUC it shows 0. Please suggest me how should i use rapidminer to get the correct AUC score on my data.

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RalfKlinkenberg

Hi Nona,
in order to be able to compute the ROC curve and the Area Under the ROC Curve (AUC), RapidMiner needs to be able to sort the classified examples/predictions by their confidence of being positive examples. Hence you also need the confidence column(s) predicted by your model.
Best regards,
Ralf

Nona

Hi Ralf
Thanks for the reply. But my algorithm does not return any probability or confidence value. How could I evaluate its performance? My dataset is imbalanced.

MartinLiebig

Hi Nona,

which algorithm are you using? I think every predictive algorithm in RM returns confidences. AUC is a measure which is only possible if you have some kind of confidence in place.

You could try to emulate a similar thing using different weights for the classes. given that you can get different recalls and precisions. Not sure how to do the calculation though.

~Martin

Nona

Hii Martin
I am not using any rapidminer algorithm. .I have designed a new algorithm and I want to compare its performance with others. I am doing all other experiments in rapidminer and that's why I imported the results of my algorithm in rapidminer to compare the results.