I just finished working through a terrific new book, “Applied Predictive Modeling,” by Max Kuhn and Kjell Johnson. K & J present a comprehensive practitioners’ guide “to the predictive modeling process and a place where one can come to learn about the approach and to gain intuition about the many commonly used and modern, powerful models.” APM’s orientation is applied and conceptual with little obsession for arcane math. And hands-on’s the goal, so there’s lots of well-written R statistical language code to learn from.

The book primarily emphasizes the supervised learning problem in which models are developed to predict an outcome or dependent variable as a function of features or independent variables. The outcome measure can be either numeric, in which case the models are called regression, or categorical, where they’re considered classification. In both cases, features can be numeric or categorical. Some of the discussed methods produce model coefficients than can be interpreted, while others are “black box”.

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