A bit of text we are proud to steal from our good friend Joseph Rickert: Then, for some very readable background material on SVMs I recommend section 13.4 of Applied Predictive Modeling and sections 9.3 and 9.4 of Practical Data Science with R by Nina Zumel and John Mount. You […]
It’s a folk theorem I sometimes hear from colleagues and clients: that you must balance the class prevalence before training a classifier. Certainly, I believe that classification tends to be easier when the classes are nearly balanced, especially when the class you are actually interested in is the rarer one. […]
This note is a link to an excerpt from my upcoming monster support vector machine article (where I work through a number of sections of [Vapnik, 1998] Vapnik, V. N. (1998), Statistical Learning Theory, Wiley). I try to run down how the original theoretical support vector machine claims are precisely […]