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Feature Selection in Machine Learning with Python covers widely used methods to select predictive subsets of features. The aim of variable selection is to improve the performance of the machine learning models, to create faster and more cost-effective algorithms, and to facilitate the understanding of the predictions returned by the models. There are many feature selection algorithms, and each one of them has advantages and limitations. Some algorithms scale well and are therefore suitable for extremely high feature spaces. However, they do not contemplate feature interactions, or the mechanisms of the predictive model. Other techniques are quite computationally expensive, yet they work well with particular classifiers or regression models. Feature Selection in Machine Learning with Python discusses many feature selection procedures, highlighting their advantages and shortcomings. Further, it provides various code recipes to select features utilizing Python open-source libraries. Throughout the book, you will be learning how to select the best possible subset of features, based on the size of your dataset, the nature of your variables, and the characteristics of the machine learning model you use. By the end of the book, you will be able to decide and implement the best selection algorithm for your data science project.