Multiclass Local Calibration With the Jensen-Shannon Distance
PositiveArtificial Intelligence
A new study introduces a method for improving the calibration of predicted probabilities in multiclass machine learning models using the Jensen-Shannon distance. This advancement is significant because well-calibrated models can enhance decision-making processes across various applications, ensuring that predictions align more closely with actual outcomes. By addressing the limitations of existing calibration techniques, this research could lead to more reliable and trustworthy AI systems.
— Curated by the World Pulse Now AI Editorial System



