This is the study notes of Applied Predictive Modeling (Kuhn and Johnson (2013)) using IPython notebook. This text, written in R, is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. The notebook reproduces book examples, provides exercise solutions and study notes for interested readers who wants to study the book using Python.
Part I General Strategies
- Ch.2 A short tour of the predictive modeling process
- Ch.3 Data pre-processing
- Ch.4 Over-fitting and model tuning
Part II Regression Models
- Ch.5 Measuring performance in regression models
- Ch.6 Linear regression and its cousins
- Ch.7 Nonlinear regression models
- Ch.8 Regression trees and rule-based models
- Ch.9 A summary of solubility models
- Ch.10 Case study: compressive strength of concrete
Part III Classification Models
- [Ch.11 Measuring performance in classification models]