Forbes

Why Your 'Accurate' AI Model Might Still Be Dangerously Wrong: The Hidden Importance Of Model Calibration

Every week, I see another headline about an AI model achieving “96% accuracy” in healthcare or finance. The press releases are exciting, the demos look impressive and stakeholders are eager to deploy.
Nature

Developing well-calibrated illness severity scores for decision support in the critically ill

Illness severity scores are regularly employed for quality improvement and benchmarking in the intensive care unit, but poor generalization performance, particularly with respect to probability ...
Risk

Addendum to Rubtsov and Petrov (2016): “A point-in-time–through-the-cycle approach to rating assignment and probability of default calibration”

In June 2016, The Journal of Risk Model Validation published a paper by Rubtsov and Petrov (2016) called “A point-in time–through-the-cycle approach to rating assignment and probability of default ...
Risk

Backtesting of a probability of default model in the point-in-time–through-the-cycle context

This paper presents a backtesting framework for a probability of default (PD) model, assuming that the latter is calibrated to both point-in-time (PIT) and through-the-cycle (TTC) levels. We claim ...