Chapter 19. When Should You Trust Your Predictions?

A predictive model can almost always produce a prediction, given input data. However, in plenty of situations it is inappropriate to produce such a prediction. When a new data point is well outside of the range of data used to create the model, making a prediction may be an inappropriate extrapolation. A more qualitative example of an inappropriate prediction would be when the model is used in a completely different context. The cell segmentation data used in Chapter 14 flags when human breast cancer cells can or cannot be accurately isolated inside an image. A model built from these data could be inappropriately applied to stomach cells for the same purpose. We can produce a prediction, but it is unlikely to be applicable to the different cell type.

This chapter discusses two methods for quantifying the potential prediction quality:

Equivocal zones

This method uses the predicted values to alert the user that results may be suspect.


This method uses the predictors to measure the amount of extrapolation (if any) for new samples.

Equivocal Results

If a model result indicated that you had a 51% chance of having contracted COVID-19, it would be natural to view the diagnosis with some skepticism. In fact, regulatory bodies often require many medical diagnostics to have an equivocal zone. This zone is a range of results in which the prediction should not be reported to patients, for example, some range of COVID-19 ...

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