Sparsification serves as a metric for quantitatively assessing the relationship between errors and uncertainties in ML models. In this context, the error is typically measured using standard metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), or the 𝑅 # score, commonly used in ML performance evaluation. The unique aspect of sparsification is the iterative removal of data points exhibiting the largest uncertainties. Following each removal step, the errors of the remaining dataset are recalculated. This process elucidates the connection between uncertainties and errors; ideally, removing the most uncertain data points should lead to a decrease in error, assuming these points also carry large errors. Hence, sparsification not only sheds light on the ranking of uncertainty relative to errors but also offers insights into the accuracy of the ML model.