Understanding the world and predicting its future outcomes has, in a sense, always been in the heart of scientific research. This has become even more true in the current day, where the volume and quality (not in all cases) of data have greatly improved, making it possible to attack even harder prediction problems than a few years ago. The availability of these data sources and the related demand for utilizing them for prediction purposes have led to a fierce development of new analytic techniques aiming to provide accurate predictions. New branches of data analysis have emerged focusing on such problems, with new names, such as Predictive Analytics. Two major scientific communities have been driving these developments forward, and even (forcefully) competing with each other, Computer Science and Statistics. The book by Max Kuhn and Kjell Johnson is somewhere on the ridge between these fields, introducing and explaining with practical examples several techniques utilized to tackle the prediction problem.

doi.org/10.1111/biom.12855, hdl.handle.net/1765/113866
Department of Biostatistics

Rizopoulos, D. (2018, March 14). Book review: Max Kuhn and Kjell Johnson. Applied Predictive Modeling. New York, Springer. Biometrics. doi:10.1111/biom.12855