Summary Probability predictions from binary regressions or machine learning methods ought to be calibrated: if an event is predicted occur with probability $x$, it should materialize approximately that frequency, which means the so-called calibration curve $p(\cdot)$ equal identity, i.e., $p(x) = x$ for all $x$ in unit interval. We propose honest assessment based on novel confidence bands curve...