Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
How Predictive AI Will Solve GenAI’s Deadly Reliability Problem
  Originally published in Forbes Generative AI too unreliable to...
5 Ways To Hybridize Predictive AI And Generative AI
  Originally published in Forbes AI is in trouble. Both...
This Simple Arithmetic Can Optimize Your Main Business Operations
 Originally published in Forbes Deep down, we all know that...
Predictive AI Usually Fails Because It’s Not Usually Valuated
 Originally published in Forbes Why in the world would the...
SHARE THIS:

8 years ago
Feature Engineering vs. Machine Learning in Optimizing Customer Behavior

 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

This content is restricted to site members. If you are an existing user, please log in on the right (desktop) or below (mobile). If not, register today and gain free access to original content and industry news. See the details here.

Comments are closed.