Galli Soledad. 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

Leanpub, 2022-08-24. — 52 p.Learn how to implement various feature selection methods in a few lines of code utilizing the open-source Python library Feature-engine.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 selecting a subset of features from the total variables in a data set to train machine learning algorithms. Feature selection is critical for developing more straightforward, faster, and highly performant machine learning models. Any feature selection algorithm aims 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 can capture feature interaction while handling feature redundancy.We will use the Python libraries Matplotlib, NumPy, Pandas, Scikit-learn, and Feature-engine.Who is this book forThis book is intended for data scientists with knowledge of machine learning and feature selection who want to learn additional methods that they can use to select features.