Applied Predictive Modeling covers the overall predictive modeling
process, beginning with the crucial steps of data preprocessing, data
splitting and foundations of model tuning. The text then provides
intuitive explanations of numerous common and modern regression and
classification techniques, always with an emphasis on illustrating and
solving real data problems. The text illustrates all parts of the
modeling process through many hands-on, real-life examples, and every
chapter contains extensive R code for each step of the process.
This multi-purpose text can be used as an introduction to predictive
models and the overall modeling process, a practitioner's reference
handbook, or as a text for advanced undergraduate or graduate level
predictive modeling courses. To that end, each chapter contains problem
sets to help solidify the covered concepts and uses data available in
the book's R package.
This text is intended for a broad audience as both an introduction to
predictive models as well as a guide to applying them. Non-mathematical
readers will appreciate the intuitive explanations of the techniques
while an emphasis on problem-solving with real data across a wide
variety of applications will aid practitioners who wish to extend their
expertise. Readers should have knowledge of basic statistical ideas,
such as correlation and linear regression analysis. While the text is
biased against complex equations, a mathematical background is needed
for advanced topics.
