Calibrated probabilistic predictions for biomedical applications

Venn Prediction (VP) is a machine learning framework that can be used to develop methods that provide well-calibrated probabilistic outputs. Unlike other probabilistic methods, the VP framework guarantees validity under the assumption that the data are independently and identically distributed (i.i.d.). Well-calibrated probabilistic outputs are of great importance, especially in biomedical applications. In this work, we develop a new Venn Predictor based on the Sequential Minimal Optimisation (SMO) algorithm and we examine its application to two real-world biomedical problems. We demonstrate in our results that our method can provide calibrated probabilistic outputs for predictions without any loss of accuracy. Moreover, we compare the outputs of our method with the probability outputs of SMO with logistic regression.