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"Feature selection" as a book category covers methods and best practices for identifying the most relevant variables from datasets to build simpler, faster, and more interpretable predictive models. Titles in this genre typically explain the theoretical foundations (statistics, information theory, and optimization), present algorithmic families (filter, wrapper, and embedded methods), and contrast feature selection with related approaches like dimensionality reduction. They also emphasize practical issues—preprocessing, evaluation and validation strategies, stability and interpretability of selected features, computational scaling, and common pitfalls such as leakage—often illustrated with case studies from domains like bioinformatics, NLP, and finance. Aimed at students, data scientists, and researchers, these books range from mathematically rigorous treatments to hands-on guides with code examples and tool-specific recipes (e.g., scikit-learn, R packages).

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