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Conformal Prediction is a framework that produces prediction intervals basedon the output from a machine learning algorithm. In this paper we explore thecase when training data is made up of multiple parts available in differentsources that cannot be pooled. We here consider the regression case and proposea method where a conformal predictor is trained on each data sourceindependently, and where the prediction intervals are then combined into asingle interval. We call the approach Non-Disclosed Conformal Prediction(NDCP), and we evaluate it on a regression dataset from the UCI machinelearning repository using support vector regression as the underlying machinelearning algorithm, with varying number of data sources and sizes. The resultsshow that the proposed method produces conservatively valid predictionintervals, and while we cannot retain the same efficiency as when all data isused, efficiency is improved through the proposed approach as compared topredicting using a single arbitrarily chosen source.

Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets