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Books in the "Feature extraction and selection" category focus on methods for creating, transforming, and choosing the most informative input variables for machine learning and statistical models. They cover theory and practical techniques—from manual feature engineering and domain-specific representation learning to automated extraction with PCA, LDA, t-SNE, autoencoders and modern representation models—and selection approaches such as filter, wrapper, and embedded methods, regularization, and feature importance measures. Typical topics include dimensionality reduction, handling high-dimensional and sparse data, evaluation and validation of feature sets, trade-offs between model complexity, performance and interpretability, and implementation in real-world pipelines and tools. These books are aimed at data scientists, ML engineers, statisticians and students seeking both mathematical foundations and applied guidance for improving model accuracy, robustness, and efficiency through better features.

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