Conformal Prediction is a distribution-free uncertainty quantification. This prediction works on any model and any dataset. Using this techniques, re-train or re-analyze the model is no longer necessary in getting the confidence interval of our prediction result.

The width of the confidence interval (Figure 1) depends on the variation of the sample and sample error. In this article, sample is the target (y) variable and sample error is the error between the actual and predicted value respectively. The greater the sampling error, the wider the confidence interval.