Introducing Conformal Prediction in Predictive Modeling. A Transparent and Flexible Alternative to Applicability Domain Determination

Conformal
prediction is introduced as an alternative approach to domain applicability
estimation. The advantages of using conformal prediction are as follows:
First, the approach is based on a consistent and well-defined mathematical
framework. Second, the understanding of the confidence level concept
in conformal predictions is straightforward, e.g. a confidence level
of 0.8 means that the conformal predictor will commit, at most, 20%
errors (i.e., true values outside the assigned prediction range).
Third, the confidence level can be varied depending on the situation
where the model is to be applied and the consequences of such changes
are readily understandable, i.e. prediction ranges are increased or
decreased, and the changes can immediately be inspected. We demonstrate
the usefulness of conformal prediction by applying it to 10 publicly
available data sets.