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[D] How to deal with a classification problem of a big mbalanced dataset?

I have a dataset of 8 million unique members, approximately 800 million records. Of those 8 million members I have a positive sample of about 25000. It’s a binary classification problem. I would like to not simply downsample although the downsampled RF performs pretty well. The data is on a Hadoop cluster. I only have access to it via a Zeppelin notebook with PySpark. It’s a pain in the ass to get approval for packages installed. PySpark is even in python 2.7 and I don’t really use Python 2. What should I do? The notebook is in a VM that’s not connected to the worldwideweb. I would have to rewrite solutions like SMOTE if I wanted to use it. I found a package but it takes like a week for approval and I only have two more weeks for the project. I wanted to use a balanced or weighted random forest but I don’t see a native spark.ml implemention. I’m also kind of new to spark.

Any tips or advice on how to proceed? Would highly appreciate.

submitted by /u/melesigenes
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.