Unbalanced data - good practice

Hi Everybody,

I have a question concerning treatment of unbalanced data.

I am trying to “machine learn” model which should detect corporate credit issues with Random Forest using KNIME. After cleaning the data i have enough records with couple of features but with proportion = 1 issue : 250 not-issue.

without any modification i have high (99%) accuracy but all categorized as “not-issue” (no detection of “issue” in validation dataset) - no suprise i suppose :slight_smile:

i have few feature which clearly have different characteristic in “issue” and “not-issue” group - for example turnover on accounts - taking into account average change: decrease in “issue” group and slight increase in “not-issue” pool.

is it in line with good practice to get rid-off of “not-issue” records with certain triggers - for example delete records with decrease in turnover in “not-issue” group as it is expected (more common) in “issue” case? after such implementation i have more balanced data = 1 issue : 40 not-issue. The outcome is also significantly better = 80% of “issues” classified correctly on validation dataset as “issues”.

do you know any other good practice to treat the unbalanced datasets? if my way is ok - how to find best trigger level for filtering through “non-issue” group?

thanks for helping