"Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the qualit
Feature Engineering for Machine Learning
β Scribed by Alice Zheng;Amanda Casari
- Publisher
- O'Reilly Media, Inc.
- Year
- 2018
- Tongue
- English
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you'll learn techniques for extracting and transforming features--the numeric representations of raw data--into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book.
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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 th
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youβll learn techniques for extracting and transforming featuresβthe numeric representations of raw dataβinto formats for machine-learning models. Each chap
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youβll learn techniques for extracting and transforming featuresβthe numeric representations of raw dataβinto formats for machine-learning models. Each chap
Intro; Copyright; Table of Contents; Preface; Introduction; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Acknowledgments; Special Thanks from Alice; Special Thanks from Amanda; Chapter 1. The Machine Learning Pipeline; Data; Tasks; Models; Features; Model E
Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key Features-: Discover solutions for feature generation, feature extraction, and feature selection Uncover the end-to-end feature engineering process across