  We present a novel algorithm that predicts the probability that the time
derivative of the horizontal component of the ground magnetic field $dB/dt$
exceeds a specified threshold at a given location. This quantity provides
important information that is physically relevant to Geomagnetically Induced
Currents (GIC), which are electric currents { associated to} sudden changes in
the Earth's magnetic field due to Space Weather events. The model follows a
'gray-box' approach by combining the output of a physics-based model with
machine learning. Specifically, we combine the University of Michigan's
Geospace model that is operational at the NOAA Space Weather Prediction Center,
with a boosted ensemble of classification trees. We discuss the problem of
re-calibrating the output of the decision tree to obtain reliable
probabilities. The performance of the model is assessed by typical metrics for
probabilistic forecasts: Probability of Detection and False Detection, True
Skill Statistic, Heidke Skill Score, and Receiver Operating Characteristic
curve. We show that the ML enhanced algorithm consistently improves all the
metrics considered.
