  We demonstrate a new type of analysis for the DRIFT-IId directional dark
matter detector using a machine learning algorithm called a Random Forest
Classifier. The analysis labels events as signal or background based on a
series of selection parameters, rather than solely applying hard cuts. The
analysis efficiency is shown to be comparable to our previous result at high
energy but with increased efficiency at lower energies. This leads to a
projected sensitivity enhancement of one order of magnitude below a WIMP mass
of 15 GeV c$^{-2}$ and a projected sensitivity limit that reaches down to a
WIMP mass of 9 GeV c$^{-2}$, which is a first for a directionally sensitive
dark matter detector.
