Comparing Performance of Different Inductive and Transductive Conformal Predictors Relevant to Drug Discovery
Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 60:201-212, 2017.
We present an evaluation of the impact of transductive, inductive, aggregated and cross inductive mondrian conformal prediction on the validity and efficiency of predictions. The aim of the study is to give guidance to which methods perform best where there is limited data. The evaluation has been made on a large public dataset of Ames mutagenicity data, relevant for drug discovery, a spam dataset and a diverse set of drug discovery datasets. When considering predictions only, the transductive conformal predictor performs the best in terms of validity. If however more information is required, for example interpretation of a prediction, then any of the methods that calculate an averaged p-value should be considered.