the framework of Millennium, the most predictive halo property for $N_{gal}$. We employ a forward feature selection algorithm using SVM to do this. To ensure the stability of our results, we re-perform the feature selection 10 times with different random draws of the training, validation and test sets. Key numbers are summarized in Table \[tab:fs\]. The MSE values quoted for each feature under the column heading “first round” correspond to the median MSE values of the 10 trials in the first round of the forward feature selection. One can see that $R_{1/2}$ has the smallest MSE in the first round of selection which indicates that it is the best predictor for $N_{gal}$. Using $R_{1/2}$ as the only feature for training gives a median MSE of 0.163 which is very close to the MSE obtained using all features as shown in Figure \[fig:milall\]. The other parameters yield MSE values ranging from 0.189 ($M_{200}$) to 0.275 (spin). The values quoted under “second round” correspond to the median MSE obtained by adding in another feature on top of $R_{1/2}$. One can see that this does not significantly change/improve on the minimum MSE from the first round. Hence it appears that most of our 