A previous post has discussed model discrimination very nicely. The post also briefly discussed calibration:
"When evaluating a risk model, calibration is also very important. To examine this, you will look at all patients with a risk score of around, e.g., 0.7, and see if approximately 70% of these actually were ill. Do this for each possible risk score (possibly using some sort of smoothing / local regression). Plot the results, and you’ll get a graphical measure of calibration.
If have a model with both good calibration and good discrimination, then you start to have good model"
I a bit confused about the meaning of calibration (see my previous post here) and would be grateful if someone could explain it in a similar fashion.