If the predicted label for a matrix element is 1, we keep the exact value of that element, and if the predicted label is 0, we simply set that matrix element to zero. Then we diagonalize the reconstructed H-matrices and compare the resulting eigenvalues with the exact eigenvalues of the exact H-matrices. In Table , we present the mean-absolute-error (MAE) of all the eigenvalues. We can see that the MAE of the eigenvalues are about 1-2 cm -1 , except for the RF model on glycine. This is probably due to the very unbalanced 0/1 label in this Hmatrix. When using a threshold of 10 -5 hartree, 98.9% of the matrix elements are labelled 0, so a small amount of wrong predictions may cause relatively large error. Nevertheless, this level of accuracy in the eigenvalues is satisfactory compared to the the errors caused by the underlying electronic structure theory, fitting precision, etc. Finally, we present the timings for training these ML models and make predictions on the matrix elements. The goal of pruning the matrix elements is to save computer times for "zeros", so the training and prediction of the ML models must at least be more efficient than actually computing those matrix elements. We only test the timings on the largest H-matrix, i.e., from 4-mode saddle point vinyl, as the matrix elements are expensive to compute in this example. The ML approach may not have the advantage if the matrix size is small to medium, since computing all matrix elements in a small or medium H-matrix is still fast, while the overheads of training ML models make this approach slower than simply computing all the elements. For this 4-mode vinyl H-matrix, training the RF model takes only 48 seconds and predicting the off-diagonal elements take 389 seconds, and the training and prediction of the MLP model takes 151 and 36 seconds, respectively. As a comparison, computing the H-matrix exactly takes hours, so the overhead of the ML model is small.