Should I clean up data that obviously does not belong?

[nishant_g (Nishant Gaurav)]

@radek

Andrew Ng in one of his lectures makes an interesting point - all such considerations should probably be driven by error analysis. For mislabeled data, you could look at the errors your model produces. If you are getting a 100 errors and 80 of them are due to mislabeled data, then that is probably a problem. If your model produces 100 errors, but only 5 of them are due to mislabeled data, then potentially your time would be better spent elsewhere to improve its performance then on cleaning up the data sets.

I think the above guidelines should be followed. But I still would like to point out, that in the Rossmann Store Sales prediction competition Jeremy talks about some outlier data points. The 3rd place winners didn’t notice it. But the first place winners realized that after and before store close, sales are unusually high. And since they don’t have the data for store closing, it can’t be modeled.

Summing up, I think removal is not going to harm you. If it is not time consuming you should do it.

Here is a post about best practices. The above point has been covered there with a link to the video where this topic was discussed.