For the test set, the recall of RegioSQM20 is similar to RegioML (81% vs 83%) but the precision is significantly worse (75% vs 88%). For the out-of-sample set, the recall is somewhat better for RegioSQM20 (80% vs 76%) but the precision is still worse (73% vs 80%), leading to a slightly smaller MCC value of 0.69 compared to 0.72 for RegioML. In contrast to RegioML, the overall performance of RegioSQM20 is very similar for the test and out-of-sample set, as one would expect from a more physics based method. However, RegioSQM20 does not offer an advantage over RegioML for the out-of-sample dataset, while being computationally much more demanding (see below).