# What are the rules / guidelines for downsampling?

I have a data set with ~ 7 million rows, of which ~ 100k are positives. I'm looking to shrink the data by keeping all the positives and then randomly sampling several hundred thousand negative examples to round out the data set.

I'm uncertain what the guidelines are though. Are there any rules of thumb for what % needs to be positive / negative?

Any suggestions would be appreciated.

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So what are you trying to achieve? What are your analysis goals? And why do you think you need to subsample? What exactly the problems are with the full data set? –  StasK Sep 4 '12 at 23:34
@Stask: I'm trying to predict whether someone will respond to a mailing (binary classification). My main goal is to work with a data set that is < 1 GB instead of one that is 10 GB. –  screechOwl Sep 4 '12 at 23:43
You don't always have to work with the 10Gb set, you could use methods like a decision tree or random forests to generate the classifier and then work with that; the classifier won't require that much information compared to the information needed to train it. –  casperOne Sep 5 '12 at 1:49