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“Model Selection” books focus on the principles and techniques for choosing the best predictive or explanatory model from a set of candidates, balancing goodness of fit with simplicity and out-of-sample performance. They cover statistical and machine learning approaches to evaluating and comparing models, such as cross-validation, information criteria (AIC, BIC), likelihood-based and Bayesian methods, regularization and sparsity, feature selection, and hyperparameter tuning. Core themes include overfitting versus underfitting, the bias–variance trade-off, interpretability, and rigorous performance assessment. Spanning theory, algorithms, and applied case studies across data science, econometrics, bioinformatics, and the social and natural sciences, this category equips students, researchers, and practitioners to compare, validate, and select models that best explain data or deliver reliable predictions.

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