The debate on this topic is not a new one. What is the secret sauce in yielding improved modelling performance? Is it the inputs, features or variables of a given predictive model or is it the specific mathematics that is used alongside these inputs or features? Historically, practitioners including myself, have tended to argue that it is the inputs or the feature engineering component which yield the most value when building models. In fact, I wrote a paper several years ago which was published in the “Journal of Marketing Analytics” –May, 2013 entitled “Is predictive analytics for marketers
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