fraction for galaxies with only photometric data. However, using a larger number of input parameters, the accuracy of the estimation may be further improved. As we move to larger numbers of input parameters, the complexity of the matrices increases, and ultimately becomes very cumbersome, such that ANN can provide a better framework for the extension into many variable space. Another important result from this section is the potential pitfalls associated with combining different datasets for training. Homogeneity is key in the training process. If the training sample is heterogeneous (either in its intrinsic properties or in the methods used to obtain measured or derived properties) the network will be compromised. Moreover, the mixed quality or depth of a combined dataset (e.g. ALFALFA+GASS) makes it very challenging to determine a unique set of control and quality parameters, which is a critical part of our analysis. It is therefore extremely rare that different datasets are (or can be) combined for training. Instead, we use a single sample for our training set (ALFALFA) and then define the regime for which it can be robustly applied to other samples. This might be done in linear examples using a simple criterion such as a cut 