Of course, the machine learning error could also be improved by simply including more training samples. As long as the learning curve in Fig. stays linear, the model will meaningfully learn when more data is added to the training set. If the current trend holds, doubling the size of the training set from 4000 to 8000 samples would reduce the machine learning density error from ϵ ρ M L = 0.62% to 0.52%. Whether this increase in accuracy is worth the extra cost of building up the training set depends on the application at hand.