Feature Engineering Made Easy

Sinan Ozdemir, Divya Susarla




A perfect guide to speed up the predicting power of machine learning algorithms

Key Features

  • Design, discover, and create dynamic, efficient features for your machine learning application
  • Understand your data in-depth and derive astonishing data insights with the help of this Guide
  • Grasp powerful feature-engineering techniques and build machine learning systems

Book Description

Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective.

You will start with understanding your data―often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data.

By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization.

What you will learn

  • Identify and leverage different feature types
  • Clean features in data to improve predictive power
  • Understand why and how to perform feature selection, and model error analysis
  • Leverage domain knowledge to construct new features
  • Deliver features based on



- 為機器學習應用程序設計、發現和創建動態高效的特徵
- 通過本指南深入了解數據,獲得驚人的數據洞察力
- 掌握強大的特徵工程技術,構建機器學習系統


您將從了解數據開始 - 您的機器學習模型的成功往往取決於如何利用不同的特徵類型,例如連續、分類等。通過了解錯誤分析和模型的可接受性,您將學習何時包含特徵,何時省略特徵以及為什麼這樣做。您將學習將問題陳述轉化為有用的新特徵。您將學習根據業務需求和數學洞察力提供特徵。您還將學習如何在機器上使用機器學習,自動學習數據的驚人特徵。


- 識別和利用不同的特徵類型
- 清理數據中的特徵,提高預測能力
- 理解為什麼以及如何進行特徵選擇和模型錯誤分析
- 利用領域知識構建新特徵
- 基於業務需求提供特徵