which the ANN was trained). The final strategy for defining a robust sample for applying the ANN is presented in Section \[sec-comb\]. In section \[sec-sum\] a short summary is presented. A public catalog of the HI gas mass fraction, the control parameters and errors accompanies this paper in electronic format.

Samples {#sec-sample}
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Training Set
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At the heart of our ANN approach is a training set based on matches between the SDSS DR7 galaxy catalog and the ALFALFA.70 data release which contains 6837 galaxies with 21 cm detections at a S/N$>$ 6.5. Following Haynes et al. (2011), we allocate a code = 1 to these objects (hereafter AC1). Haynes et al. (2011) also identify lower S/N detections (code = 2, hereafter, AC2). Although AC2 galaxies have been matched to galaxies in the SDSS and are therefore likely detections, we take the conservative measure of not including them as detections in our training sample. Our analysis also makes use of galaxies not detected by ALFALFA. Non-detections of SDSS galaxies are not explicitly reported in the ALFALFA data releases, so we make use of the non-detections for the ALFALFA.40 footprint as computed by Ellison et al. (2015). The 23,652 non-detections are 