  In the field of chemistry, there have been many attempts to predict the
properties of unknown compounds from statistical models constructed using
machine learning. In an area where many known compounds are present (the
interpolation area), an accurate model can be constructed. In contrast, data in
areas where there are no known compounds (the extrapolation area) are generally
difficult to predict. However, in the development of new materials, it is
desirable to search this extrapolation area and discover compounds with
unprecedented physical properties. In this paper, we propose Stochastic
Threshold Model Trees (STMT), an extrapolation method that reflects the trend
of the data, while maintaining the accuracy of conventional interpolation
methods. The behavior of STMT is confirmed through experiments using both
artificial and real data. In the case of the real data, although there is no
significant overall improvement in accuracy, there is one compound for which
the prediction accuracy is notably improved, suggesting that STMT reflects the
data trends in the extrapolation area. We believe that the proposed method will
contribute to more efficient searches in situations such as new material
development.
