An Introduction to Statistical Learning: With Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, 2021 (Springer) - This widely-used textbook provides an accessible introduction to classification, explaining how models output probabilities and how decision thresholds are applied to make predictions.
Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006 (Springer) - A classic and comprehensive textbook that offers a rigorous treatment of probabilistic classification models and the principles behind converting scores into discrete class labels.
CS229 Machine Learning Course Notes, Andrew Ng, 2022 (Stanford University) - These widely-referenced lecture notes from a prominent machine learning course clearly explain the concept of classification, model confidence (probabilities), and the role of a decision boundary in making final predictions.