Conformal FRP@TPR95 Metric

Conformal prediction methods are being actively researched to provide reliable uncertainty quantification for diverse machine learning models, addressing limitations of traditional point estimates. Current work focuses on integrating conformal prediction with various architectures, including neural networks, diffusion models, and tree-based algorithms, to improve the robustness and trustworthiness of predictions across applications such as botnet detection, cellular network quality estimation, and 3D pose estimation. This enhanced uncertainty quantification is crucial for improving the reliability and safety of machine learning deployments in high-stakes domains, offering valuable insights into model performance and guiding data collection strategies.