  We developed a flare prediction model using machine learning, which is
optimized to predict the maximum class of flares occurring in the following 24
h. Machine learning is used to devise algorithms that can learn from and make
decisions on a huge amount of data. We used solar observation data during the
period 2010-2015, such as vector magnetogram, ultraviolet (UV) emission, and
soft X-ray emission taken by the Solar Dynamics Observatory and the
Geostationary Operational Environmental Satellite. We detected active regions
from the full-disk magnetogram, from which 60 features were extracted with
their time differentials, including magnetic neutral lines, the current
helicity, the UV brightening, and the flare history. After standardizing the
feature database, we fully shuffled and randomly separated it into two for
training and testing. To investigate which algorithm is best for flare
prediction, we compared three machine learning algorithms: the support vector
machine (SVM), k-nearest neighbors (k-NN), and extremely randomized trees
(ERT). The prediction score, the true skill statistic (TSS), was higher than
0.9 with a fully shuffled dataset, which is higher than that for human
forecasts. It was found that k-NN has the highest performance among the three
algorithms. The ranking of the feature importance showed that the previous
flare activity is most effective, followed by the length of magnetic neutral
lines, the unsigned magnetic flux, the area of UV brightening, and the time
differentials of features over 24 h, all of which are strongly correlated with
the flux emergence dynamics in an active region.
