Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson

The process of developing predictive models includes many stages. Most resources focus on the modelling algorithms, but neglect other critical aspects of the modelling process. This book describes techniques for finding the best representations of predictors for modelling and for finding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques, along with R programs for reproducing the results.

Table of Contents:
1. Introduction
2. Illustrative Example: Predicting Risk of Ischemic Stroke
3. A Review of the Predictive Modeling Process
4. Exploratory Visualizations
5. Encoding Categorical Predictors
6. Engineering Numeric Predictors
7. Detecting Interaction Effects
8. Handling Missing Data
9. Working with Profile Data
10. Feature Selection Overview
11. Greedy Search Methods
12. Global Search Methods

Links:
- http://www.feat.engineering/
- https://www.routledge.com/Feature-Engineering-and-Selection-A-Practical-Approach-for-Predictive-Models/Kuhn-Johnson/p/book/9781138079229
- https://www.routledge.com/Feature-Engineering-and-Selection-A-Practical-Approach-for-Predictive-Models/Kuhn-Johnson/p/book/9781138079229

Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearning #ml #featureengineering #featureselection #missingdata #categoricalvariables

@accelerated_learning