The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009 (Springer)DOI: 10.1007/978-0-387-84858-7 - This classic textbook provides a comprehensive statistical foundation for logistic regression, covering the sigmoid function, maximum likelihood estimation (which leads to log loss), decision boundaries, and its role as a generalized linear model.
Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006 (Springer)DOI: 10.1007/978-0-387-44958-3 - A highly regarded textbook that covers logistic regression from a probabilistic perspective, detailing the derivation of the likelihood function, cross-entropy error, and optimization methods.
sklearn.linear_model.LogisticRegression, Scikit-learn developers, 2023 - Official documentation for Scikit-learn's Logistic Regression class, detailing parameters such as C, penalty, solver, as well as methods like predict_proba, and handling of multi-class problems.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - A practical guide that effectively bridges theoretical concepts of logistic regression with its implementation in Scikit-learn, including explanations of regularization and hyperparameter tuning.