Automated Predictive Analytics – What Could Possibly Go Wrong?

Much of that is in data cleansing, normalizing, removing skewness, transforming data for specific algorithm requirements, and even running multiple algorithms in parallel to determine champion models. So long as we are talking about things like removing skewness, or normalizing data required for specific algorithms (e.g. Feature selection is fairly straightforward to automate (leaving the creative feature engineering issues aside). I wouldn't mind seeing that sort of comparative data published for all advanced analytic platforms, keeping in mind that two data scientists using the same platform can come up with different results.