Impact of non-normal error distributions on the benchmarking and ranking of Quantum Machine Learning models
Pascal Pernot,
Bing Huang,
Andreas Savin
Abstract:Quantum machine learning models have been gaining significant traction within atomistic simulation communities. Conventionally, relative model performances are being assessed and compared using learning curves (prediction error vs. training set size). This article illustrates the limitations of using the Mean Absolute Error (MAE) for benchmarking, which is particularly relevant in the case of non-normal error distributions. We analyze more specifically the prediction error distribution of the kernel ridge regr… Show more
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