or luminosity that is monotonically related to a subhalo property.

While both methods have been shown to produce mock galaxy catalogs that match observations reasonably well, it would be progressive to attempt to eliminate the above mentioned assumptions. The sophisticated non-parametric regression algorithms that form a subcategory of “machine learning” (ML) are ideal for this purpose. To obtain the mapping from halos to $N_{gal}$, the only assumptions that these model-independent algorithms require are that such a relationship exists and that it is a continuous function of the halo parameters. They then proceed to construct a model from the data itself and hence do not impose any pre-supposed relationships onto the data. We note that although ML algorithms are non-parametric in the sense that we do not need to assume a known relationship between data parameters, they do often require us to assume some operational parameters such as how severely poor predictions are penalized. These, however, can be optimized as described in §\[sec:svm\].

In addition, the ML-based approaches we propose here, like HOD-based methods, rely only on the properties of the parent dark matter halo. Hence, we can circumvent the difficulties in subhalo identification. Another point worth highlighting is that in 