Uneven distributed binominal data
Dear RM community,
I have a problem handling my dataset. I am trying to build a random forest model with a binominal label. The only prblem is, that the dataset contains 50 positives and 200 negatives. If all examples are predicted als false the accuracy is still quite OK (80%). And this is exactly what happens: Most models I get are predicting most as false.
So my question is, how to handle uneven distributed datasets. Is there for example a way to weight correct positives more than correct negatives negatives? A correct predicted postive should then be 200/50 times more valueable.
Cheers,
Markus
I have a problem handling my dataset. I am trying to build a random forest model with a binominal label. The only prblem is, that the dataset contains 50 positives and 200 negatives. If all examples are predicted als false the accuracy is still quite OK (80%). And this is exactly what happens: Most models I get are predicting most as false.
So my question is, how to handle uneven distributed datasets. Is there for example a way to weight correct positives more than correct negatives negatives? A correct predicted postive should then be 200/50 times more valueable.
Cheers,
Markus
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you can use the 'Sample' Operator set to absolute sampling with checked 'balance data' option before starting the X-Validation. This way you can ensure you have equal sized data for all your classes.
Furthermore you can try to use the 'Generate Weight (Stratification)' Operator. Classes with less examples will get higher weights then classes with more examples.
Best,
Nils