towards under-predicting $N_{gal}$. This is likely due to the fact that the number of halos with small $N_{gal}$ dominates the overall distribution while halos with high $N_{gal}$ are much rarer (see Figure \[fig:mildist\]). Then, taking kNN as an example, even if a halo should have a large number of galaxies, its nearest neighbours may still be dominated by $N_{gal}=1$ halos, which will lead the algorithm to underpredict. The current training set is clearly incomplete for halos with large $N_{gal}$. A larger training sample will have a proportionately larger number of these halos so using a larger training set should be able to partially mitigate this problem. In addition, recall that the best kernel and parameters ($C$ and $\gamma$) for the SVM and number of nearest neighbours $k$ for the kNN are selected using the validation set on the basis of minimizing MSE. Since the MSE is a balanced measure of variance and bias, one can imagine giving a different weighting to the bias, i.e. penalizing the MSE more if the bias is high. This can potentially reduce the amount of underprediction we see, however, we will pay the price of having a larger scatter in our predictions.

For SVM we 