Over the next few blog posts, which may be intermittent, but hopefully with smaller gaps than the last couple of gaps, we are going to take a sideways tour into predictive modelling, which is closer to what I am currently doing than strictly actuarial studies. Just as before, for me the purpose is to force close study, and if others can benefit, that’s a bonus.

Recently I received from a riparian bookselling website the book Applied Predictive Modeling (Kuhn and Johnson, 2013) (note one ‘l’) , having ordered it only three months earlier. As the title suggests, the thrust of this text is introduce predictive modeling techniques (whether originating as data mining or statistical techniques) in the context of their application to problem solving, rather than with respect to their theoretical origins or with a view to critiquing them, mathematically or otherwise. In fact, the authors suggest The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

(Hastie, et al) as a good theoretical companion.

As a device for forcing reading with a critical mind, I propose to read and compare the sections of both books dealing with the same topics, starting with the topics I am personally most familiar with, before moving to a couple of areas newer to me. Part of the object is to discover or partially uncover where the practical and theoretical are different and where one ways gives way for the other and back again.

Before the end of this tour we will also look at the sections on data pre-processing and ‘other considerations’ which bookend the discussions of individual modelling techniques. In some ways these sections are the most important, as they provide an especial opportunity for the authors to discuss the practice of modelling, the book’s raison d’être and strength, as well as being the areas in this text that are least often discussed in other texts.

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Tags: Logistic regression, non -linear regression, Predictive modeling, statistics

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