Effective Confidence Region Prediction Using Probability Forecasters
This work addresses confidence region prediction for applications like medical diagnostics, where providing guarantees on capturing true labels is crucial, but it is incremental as it builds on existing probability forecasting methods.
The paper tackled the problem of generating confidence region predictions from probability forecasts, aiming for well-calibrated and narrow predictions. The results showed that approximately 44% of experiments on 15 multi-class datasets achieved well-calibrated predictions, with K-Nearest Neighbor performing consistently well.
Confidence region prediction is a practically useful extension to the commonly studied pattern recognition problem. Instead of predicting a single label, the constraint is relaxed to allow prediction of a subset of labels given a desired confidence level 1-delta. Ideally, effective region predictions should be (1) well calibrated - predictive regions at confidence level 1-delta should err with relative frequency at most delta and (2) be as narrow (or certain) as possible. We present a simple technique to generate confidence region predictions from conditional probability estimates (probability forecasts). We use this 'conversion' technique to generate confidence region predictions from probability forecasts output by standard machine learning algorithms when tested on 15 multi-class datasets. Our results show that approximately 44% of experiments demonstrate well-calibrated confidence region predictions, with the K-Nearest Neighbour algorithm tending to perform consistently well across all data. Our results illustrate the practical benefits of effective confidence region prediction with respect to medical diagnostics, where guarantees of capturing the true disease label can be given.