How to use Conformalized Quantile Regression
We must know how sure our model is about its predictions to make well-informed decisions. Hence, returning only a point prediction is not enough. It does not tell us anything about whether we can trust our model or not. If you want to know why, check out my article below.
In the article, I use a classification problem as an example. However, many real-world problems are regression problems. For example, we want to know how certain a model is when predicting tomorrow’s temperature.
As the temperature is a continuous variable, we would like to know in which interval the true temperature will lie.
The wider the interval, the more uncertain the model. Hence, we should trust it less when making decisions.
Two approaches come to mind. Either we use a set of models that predict the interval or we turn a point prediction into a…
