The Art of Feature Engineering: Essentials for Machine Learning

Duboue, Pablo

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商品描述

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

商品描述(中文翻譯)

當機器學習工程師處理資料集時,他們可能會發現結果不如預期。除了改進模型或收集更多資料外,他們可以使用特徵工程的過程來改善結果,通過修改資料的特徵來更好地捕捉問題的本質。這本實用指南對於任何資料科學家或機器學習工程師來說都是必不可少的工具,提供了改善機器學習解決方案性能的新思路。從基本概念和技術開始,本書逐步介紹了一種獨特的跨領域方法,涵蓋了圖形、文本、時間序列和圖像等不同類型的資料,並提供了完整的案例研究。關鍵主題包括分箱、out-of-fold估計、特徵選擇、降維和編碼可變長度資料。案例研究的完整原始碼可在附帶網站上作為Python Jupyter筆記本下載。