  We attempt to forecast M-and X-class solar flares using a machine-learning
algorithm, called Support Vector Machine (SVM), and four years of data from the
Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first
instrument to continuously map the full-disk photospheric vector magnetic field
from space. Most flare forecasting efforts described in the literature use
either line-of-sight magnetograms or a relatively small number of ground-based
vector magnetograms. This is the first time a large dataset of vector
magnetograms has been used to forecast solar flares. We build a catalog of
flaring and non-flaring active regions sampled from a database of 2,071 active
regions, comprised of 1.5 million active region patches of vector magnetic
field data, and characterize each active region by 25 parameters. We then train
and test the machine-learning algorithm and we estimate its performances using
forecast verification metrics with an emphasis on the True Skill Statistic
(TSS). We obtain relatively high TSS scores and overall predictive abilities.
We surmise that this is partly due to fine-tuning the SVM for this purpose and
also to an advantageous set of features that can only be calculated from vector
magnetic field data. We also apply a feature selection algorithm to determine
which of our 25 features are useful for discriminating between flaring and
non-flaring active regions and conclude that only a handful are needed for good
predictive abilities.
