  Regression machine learning is widely applied to predict various materials.
However, insufficient materials data usually leads to a poor performance. Here,
we develop a new voting data-driven method that could generally improve the
performance of regression learning model for accurately predicting properties
of materials. We apply it to investigate a large family (2135) of
two-dimensional hexagonal binary compounds focusing on ferroelectric properties
and find that the performance of the model for electric polarization is indeed
greatly improved, where 38 stable ferroelectrics with out-of-plane polarization
including 31 metals and 7 semiconductors are screened out. By an unsupervised
learning, actionable information such as how the number and orbital radius of
valence electrons, ionic polarizability, and electronegativity of constituent
atoms affect polarization was extracted. Our voting data-driven method not only
reduces the size of materials data for constructing a reliable learning model
but also enables to make precise predictions for targeted functional materials.
