  The last couple of decades have seen an emergence of transient detection
facilities in various avenues of time domain astronomy which has provided us
with a rich dataset of transients. The rates of these transients have
implications in star formation, progenitor models, evolution channels and
cosmology measurements. The crucial component of any rate calculation is the
detectability and space-time volume sensitivity of a survey to a particular
transient type as a function of many intrinsic and extrinsic parameters. Fully
sampling that multi-dimensional parameter space is challenging. Instead, we
present a scheme to assess the detectability of transients using supervised
machine learning. The data product is a classifier that determines the
detection likelihood of sources resulting from an image subtraction pipeline
associated with time domain survey telescopes, taking into consideration the
intrinsic properties of the transients and the observing conditions. We apply
our method to assess the space-time volume sensitivity of type Ia supernovae
(SNe~Ia) in the intermediate Palomar Transient Factory (iPTF) and obtain the
result,$\langle VT\rangle_{\mathrm{Ia}}=2.93\pm 0.21\times
10^{-2}\mathrm{Gpc^{3}yr}$. With rate estimates in the literature, this volume
sensitivity gives a count of $680-1160$ SNe~Ia detectable by iPTF which is
consistent with the archival data. With a view toward wider applicability of
this technique we do a preliminary computation for long-duration type IIp
supernovae (SNe~IIp) and find $\langle
VT\rangle_{\mathrm{IIp}}=7.80\pm0.76\times10^{-4}\mathrm{Gpc^{3}yr}$. This
classifier can be used for computationally fast space-time volume sensitivity
calculation of any generic transient type using their lightcurve properties.
Hence, it can be used as a tool to facilitate calculation of transient rates in
a range of time-domain surveys, given suitable training sets.
