Feature Engineering and Selection: A Practical Approach for Predictive Models (Hardcover)
暫譯: 特徵工程與選擇：預測模型的實用方法 (精裝版)

Kuhn, Max, Johnson, Kjell

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

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商品描述

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.

商品描述(中文翻譯)

開發預測模型的過程包括許多階段。大多數資源專注於建模演算法，但忽略了建模過程中的其他關鍵方面。本書描述了尋找最佳預測變數表示法的技術，以及尋找最佳預測變數子集以改善模型性能的技術。使用多種示例數據集來說明這些技術，並提供 R 程式碼以重現結果。

作者簡介

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.

作者簡介(中文翻譯)

Max Kuhn, Ph.D., 是 RStudio 的軟體工程師。他在藥物發現和醫療診斷領域工作了 18 年，將預測模型應用於實際數據。他撰寫了許多用於預測建模和機器學習的 R 套件。

Kjell Johnson, Ph.D., 是 Stat Tenacity 的擁有者和創始人，該公司提供統計和預測建模的諮詢服務。他曾為美國品質協會、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 共同撰寫了 Applied Predictive Modeling，這是一本全面且實用的預測模型建構過程指南。該書於 2014 年獲得 Technometrics Ziegel 獎，表彰其卓越的書籍。

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