We can use the following methods for calibration in Supervised learning:
Platt Calibration: In Platt calibration, we transform the outputs of a classification model into a probability distribution over classes. In binary classification, we distribute data over two classes. But sometimes we need prediction about class as well as the probability of certainty about the prediction. By using Platt calibration we can get the probability estimate. It means we get how sure we are of our classification being correct.
Isotonic Regression: It is also known as monotonic regression. We use Isotonic regression to calibrate the linearity imposed by linear regression. In Isotonic regression, we fit an isotonic curve to means of an experimental result. Isotonic regression is not constrained by any concrete form like a linear function in linear regression.