Feature Selection in Machine Learning with Feature-engine
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Feature Selection in Machine Learning with Feature-engine

Discover feature selection algorithms that scale well and overcome the limitations of statistical models or the computational cost of wrapper methods.

About the Book

Feature-engine is an open-source Python library for feature engineering and feature selection. It uses pandas and Scikit-learn under the hood to engineer and select feature subsets.

Feature selection is the process of selecting a subset of features from the total variables in a data set to train machine learning algorithms. Feature selection is key for developing simpler, faster, and highly performant machine learning models. The aim of any feature selection algorithm is to create classifiers or regression models that run faster and whose outputs are easier to understand by their users.

In this book, you will find feature selection methods described in scientific literature and used in data science competitions to select the best subsets of predictor variables from your data. These methods extend the feature selection toolkit already provided by Scikit-learn, with additional tools that scale better than wrapper methods, overcome the limitations of statistical methods, and are able to capture feature interaction while handling feature redundancy.

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    • Python
    • Programming Cookbooks
    • Machine Learning
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About the Author

Soledad Galli
Soledad Galli, PhD

Soledad Galli is a data scientist, instructor, and software developer with more than 10 years of experience in world-class academic institutions and renowned businesses. She has developed and put into production machine learning models to assess insurance claims, credit risk, and prevent fraud.

Sole teaches online courses on machine learning, which have enrolled 40,000+ students worldwide and consistently receive good student reviews. She is also the developer and maintainer of the open-source Python library Feature-engine, which is currently downloaded about 100k+ times per month.

Sole received a Data Science Leaders' award in 2018 and was recognized as one of LinkedIn's voices in data science and analytics in 2019. She is passionate about sharing her machine learning knowledge. She gave talks at data science conferences and wrote several publications about data science and machine learning, including one on the misuse of data and artificial intelligence.

Soledad Galli, PhD

Episode 266

Table of Contents

  • Preface
    • Who is this book for
    • What this book covers
    • Technical requirements
    • Download the code files
    • Get in touch
  • Chapter 1: Feature Selection Overview
    • What is feature selection?
    • Why do we select features?
    • Feature selection methods
    • Univariate and multivariate methods
  • Chapter 2: Basic Feature Selection Methods
    • Constant features
    • Quasi-constant features
    • Duplicated features
    • References
  • Chapter 3: Correlation of Predictors
    • Correlation coefficients
    • Remove correlated features: retain first, remove the rest
    • Remove correlated features: retain best feature, remove the rest
  • Chapter 4: Univariate Feature Selection
    • Single feature model
    • Target encoding
    • References
  • Chapter 5: Multivariate Feature Selection
    • Recursive feature addition
    • Recursive feature elimination
    • Feature shuffling
    • References
  • Next steps
    • Other books by the author
    • Online courses by the author

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