  We study the predictive capabilities of magnetic feature properties (MF)
generated by Solar Monitor Active Region Tracker (SMART) for solar flare
forecasting from two datasets: the full dataset of SMART detections from 1996
to 2010 that has been previously studied by Ahmed et al. (2011) and a subset of
that dataset which only includes detections that are NOAA active regions (ARs).
Main contributions: we use marginal relevance as a filter feature selection
method to identify most useful SMART MF properties for separating flaring from
non-flaring detections and logistic regression to derive classification rules
to predict future observations. For comparison, we employ a Random Forest,
Support Vector Machine and a set of Deep Neural Network models, as well as
Lasso for feature selection. Using the linear model with three features we
obtain significantly better results (TSS=0.84) to those reported by Ahmed et
al.(2011) for the full dataset of SMART detections. The same model produced
competitive results (TSS=0.67) for the dataset of SMART detections that are
NOAA ARs which can be compared to a broader section of flare forecasting
literature. We show that more complex models are not required for this data.
