Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models, 2nd Edition

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Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models, 2nd Edition

  • Author:Soledad Galli
  • Length: 386 pages
  • Edition: 1
  • Publisher: Packt Publishing
  • Publication Date: 2022-10-31
  • ISBN-10: 1804611301
  • ISBN-13: 9781804611302
  • Download:Register/Login to Download
  • Buy Print:Buy from amazon



    Book Description

    Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python libraries

    Key Features

    • Learn and implement feature engineering best practices
    • Reinforce your learning with the help of multiple hands-on recipes
    • Build end-to-end feature engineering pipelines that are performant and reproducible

    Book Description

    Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to acce法拉利lerate the proc芬迪ess via a plethora of practical, hands-on recipes.

    This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you’ll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner.

    By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.

    What you will learn

    • Impute missing data using various univariate and multivariate methods
    • Encode categorical variables with one-hot, ordinal, and count encoding
    • Handle highly cardinal categorical variables
    • Transform, discretize, and scale your variables
    • Create variables from date and time with pandas and Feature-engine
    • Combine variables into new features
    • Extract features from text as well as from transactional data with Featuretools
    • Create featur罗西尼es from time series data with tsfresh

    Who this book is for

    This book is for machine learning and data science students and professionals, as well as software engineers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way.

    中文:

    书名:Python功能工程食谱: 70多种创建、工程和转换功能以构建机器学习模型的食谱,第二版

    创建可以使用开源Python库将其部署到生产中的端到端,可复制的功能工程管道

    主要功能

    • 学习并实施功能工程最佳实践
    • 加强你的学习帮助多个动手食谱
    • 构建高性能、可复制的端到端功能工程管道

    图书描述

    功能工程,转换变量和创建功能的过程,尽管很耗时,但可确保您的机器学习模型无缝执行。Python功能工程食谱的第二版将通过向您展示如何使用开源Python库通过大量实用的动手食谱来加速该过程,从而摆脱功能工程的斗争。

    此更新版本首先解决基本数据挑战,例如缺少数据和分类值,然后再进行处理偏斜分布和异常值的策略。最后几章向您展示了如何从各种类型的数据 (包括文本、时间序列和关系数据库) 开发新功能。在众多开源Python库的帮助下,您将学习如何以高性能、可复制和优雅的方式实现每种功能工程方法。

    到本Python书结束时,您将拥有自信地构建可部署到生产中的端到端和可复制功能工程管道所需的工具和专业知识。

    什么你会学到

    • 使用各种单变量和多变量方法估算缺失数据
    • 用one-hot、ordinal、count编码分类变量
    • 处理高基数分类变量
    • 转换、离散化和缩放变量
    • 使用pandas和Feature-engine创建日期和时间变量
    • 将变量合并为新功能
    • 使用Featuretools从文本和事务数据中提取特征
    • 使用tsfresh从时序数据创建特征

    这本书是为谁准备的

    本书面向机器学习和数据科学的学生和专业人士,以及从事机器学习模型部署的软件工程师,他们希望了解更五粮液多有关如何转换其数据并创建新功能以更好的方式训练机器学习模型的信息。

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