Feature Engineering and Selection: A Practical Approach for Predictive Models (Hardcover)

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding 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.

開發預測模型的過程包含許多階段。大部分資源都著重於建模演算法，但忽略了建模過程中其他關鍵的方面。本書描述了尋找最佳預測變數表示和改善模型性能的最佳預測變數子集的技術。書中使用了多種範例數據集來說明這些技術，並提供了用於重現結果的 R 程式。

Max Kuhn, Ph.D., is a software engineer at RStudio. He worked in 18 years in drug discovery and medical diagnostics applying predictive models to real data. He has authored numerous R packages for predictive modeling and machine learning.

Kjell Johnson, Ph.D., is the owner and founder of Stat Tenacity, a firm that provides statistical and predictive modeling consulting services. He has taught short courses on predictive modeling for the American Society for Quality, American Chemical Society, International Biometric Society, and for many corporations.

Kuhn and Johnson have also authored Applied Predictive Modeling, which is a comprehensive, practical guide to the process of building a predictive model. The text won the 2014 Technometrics Ziegel Prize for Outstanding Book.

Max Kuhn博士是RStudio的軟體工程師。他在藥物發現和醫學診斷領域工作了18年，應用預測模型於真實數據。他撰寫了許多用於預測建模和機器學習的R套件。

Kjell Johnson博士是Stat Tenacity的所有者和創始人，該公司提供統計和預測建模的諮詢服務。他曾為美國品質學會、美國化學學會、國際生物統計學會以及許多企業教授預測建模的短期課程。

Kuhn和Johnson還合著了《應用預測建模》，這是一本全面而實用的建立預測模型過程指南。該書贏得了2014年《Technometrics》Ziegel優秀書籍獎。