ML vs. previous methods. The x-axis shows the “true” line-of-sight Log(N_H) values, as determined by spectral fitting. The y-axis shows the Log(N_H) values predicted by our machine learning algorithm (blue circles) and those predicted by the Asmus et al. (2015) equation (orange stars). Our algorithm shows superior predictive capabilities, particularly for lower levels of obscuration (Log(N_H) < 23), where our algorithm does not incorrectly classify unobscured sources as heavily obscured as displayed by the dash-dotted gray line. The dotted black line represents the one-to-one ratio between the true and predicted N_H values. The uncertainties were calculated statistically based on the different classifications listed in Table 1. We determine the error that needs to be added or subtracted to the predicted NH values in order to achieve a 90% classification accuracy in each bin. Thus, each of the four classification bins have different uncertainties. No errors are included on the orange points for readability purposes.