  Recently, there has been growing interest in the use of machine-learning
methods for predicting solar flares. Initial efforts along these lines employed
comparatively simple models, correlating features extracted from observations
of sunspot active regions with known instances of flaring. Typically, these
models have used physics-inspired features that have been carefully chosen by
experts in order to capture the salient features of such magnetic field
structures. Over time, the sophistication and complexity of the models involved
has grown. However, there has been little evolution in the choice of feature
sets, nor any systematic study of whether the additional model complexity is
truly useful. Our goal is to address these issues. To that end, we compare the
relative prediction performance of machine-learning-based, flare-forecasting
models with varying degrees of complexity. We also revisit the feature set
design, using topological data analysis to extract shape-based features from
magnetic field images of the active regions. Using hyperparameter training for
fair comparison of different machine-learning models across different feature
sets, we show that simpler models with fewer free parameters \textit{generally
perform better than more-complicated models}, ie., powerful machinery does not
necessarily guarantee better prediction performance. Secondly, we find that
\textit{abstract, shape-based features contain just as much useful
information}, for the purposes of flare prediction, as the set of hand-crafted
features developed by the solar-physics community over the years. Finally, we
study the effects of dimensionality reduction, using principal component
analysis, to show that streamlined feature sets, overall, perform just as well
as the corresponding full-dimensional versions.
