  Recently supervised machine learning has been ascending in providing new
predictive approaches for chemical, biological and materials sciences
applications. In this Perspective we focus on the interplay of machine learning
algorithm with the chemically motivated descriptors and the size and type of
data sets needed for molecular property prediction. Using Nuclear Magnetic
Resonance chemical shift prediction as an example, we demonstrate that success
is predicated on the choice of feature extracted or real-space representations
of chemical structures, whether the molecular property data is abundant and/or
experimentally or computationally derived, and how these together will
influence the correct choice of popular machine learning algorithms drawn from
deep learning, random forests, or kernel methods.
