Practical Conformal Prediction with Python: Build Reliable and Trustworthy AI with Uncertainty Quantification, Prediction Intervals, Model Calibration, and Conformal Prediction in Python

Most machine learning models tell you what they predict, but few tell you how much to trust that prediction. Practical Conformal Prediction with Python changes that — presenting Conformal Prediction(CP) as a robust, distribution-free, model-agnostic framework for generating statistically valid confidence intervals across diverse predictive tasks in Python.

You begin with the problem of model miscalibration and the mathematical foundations of Conformal Prediction, then advance through practical CP techniques for classification, regression, and forecasting using scikit-learn and statsmodels. Each chapter blends theory with implementation through structured experiments, reproducible examples, and chapter-end quizzes.