Intelligent Model Calibration for Improved Prediction Accuracy

Abstract

Model calibration is essential for improving the reliability of predictive analytics, machine learning, and intelligent decision-support systems. While traditional evaluation metrics such as accuracy, precision, and recall measure classification performance, they do not assess the confidence of model predictions. Calibration aligns predicted probabilities with actual outcomes, enhancing trustworthiness and interpretability. This study proposes an intelligent model calibration framework that integrates adaptive calibration techniques, including Platt Scaling, Isotonic Regression, Bayesian Calibration, Ensemble Calibration, Temperature Scaling, and Meta-Learning-based Calibration. The framework incorporates data preprocessing, baseline model development, calibration parameter optimization, uncertainty estimation, and performance evaluation. Metrics such as Brier Score, Expected Calibration Error (ECE), Maximum Calibration Error (MCE), and Log-Loss are used to assess reliability. Experimental results on benchmark datasets show that calibrated models significantly reduce calibration errors, improve probability estimates, enhance predictive performance, and increase robustness under varying data distributions and uncertainty levels. The findings highlight intelligent calibration as a key mechanism for building trustworthy AI systems and improving decision-making in high-stakes applications.