From the course: Advanced Predictive Modeling: Mastering Ensembles and Metamodeling

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Combining supervised and unsupervised

Combining supervised and unsupervised - SPSS Tutorial

From the course: Advanced Predictive Modeling: Mastering Ensembles and Metamodeling

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Combining supervised and unsupervised

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- [Instructor] Okay, let's talk about a real world example of meta modeling. You want to think of a meta model like an assembly line. You're dealing with different aspects of the business problem. Now, if you've never seen IBM SPSS Modeler, this is what it looks like. Don't worry about it, we're not going to worry about teaching you how to use the software right now, we're simply going to treat this like a flow chart. If you happen to be a Modeler fan, I'll provide the .str file that you're looking at here. Okay, what we're doing is we're dealing with a dataset that's actually a bit famous, goes back many years. It is the 1999 KDD Competition dataset. And it's a big data file. It's five million records, but very frequently, folks use a 10% subsample of that, which is what I've done, about a half million records. And there are dozens and dozens of technical input variables. Wrong_fragment, whether or not it was in, the number of urgent requests, number of roots, number of shells, these…

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