(bottom). Each plot has 2 panels, the upper panel shows the actual correlation functions and the bottom panel shows the percent difference (i.e. $100 \times [\xi_{predict}(r)-\xi_{true}(r)]/\xi_{true}(r)$) between the correlation function calculated from the predicted galaxies in the test set versus that calculated from the true galaxies in the test set.

In the SVM case we see that at small scales ($\sim5{h^{-1}\rm{Mpc}}$), the predicted correlation function has a lower amplitude than the true correlation function by a significant amount ($\sim40\%$). For kNN, the difference is fairly benign ($\sim10\%$). At these small scales, the 1-halo term is still important and hence the fact that we underpredict the number of halos with $N_{gal}>1$ as shown in Figure \[fig:mildist\] will result in lower clustering amplitudes than expected. However, for analyses of large-scale structure, we are more interested in $\xi(r)$ at $r>30{h^{-1}\rm{Mpc}}$ where our predicted $\xi(r)$ is only $\sim5-10\%$ lower than the true correlation function for the most part. As the amount of underprediction is fairly constant, one can even imagine implementing a simple scaling correction for crude applications. Hence, the potential for making large-scale structure mocks using SVM or kNN remains worthy of further investigation.

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As mentioned above, there is a slight bias 