A Data Science Central Community
Predictive modeling tools and services are undergoing an inevitable step-change which will free data scientists to focus on applications and insight, and result in more powerful and robust models than ever before. Amongst the key enabling technologies are new hugely scalable cross-validation frameworks, and meta-learning.…Continue
One mantra we chant frequently is "trust the data". In the context that we use this expression it is often wise. For example: when requested for the facility to adjust the rules of a robustly tested machine-learnt model so that it better jibes with intuition; or when tempted to cherry-pick fields and features which one assumes (be it through years of domain experience or otherwise) enshrine the relevant information.
This doesn't mean that the data is always right of…Continue
You know that you want to build a predictive model. You've framed your problem in terms of classification or regression. You've prepared some training data (which took an age). Now there's just the small matter of choosing an appropriate algorithm.
You've heard or experienced first hand that Random Forests, Elastic Net Regression or Deep Belief Networks are "the business" and so you're going to use one of these (you've probably already verified that these algorithms are appropriate to…Continue
I read two strangely similar articles last week. One was an article by Vincent Granville, entitled "The 8 worst predictive modeling techniques". The other was an article on Forbes entitled "America's 10 Best-Paying Jobs".
What on Earth do these two articles have in common (other than both being lists)?
A brief flick through the Forbes article reveals that it…Continue