The prediction of the initial reaction rate in the tungsten-catalyzed epoxidation
of alkenes by using a machine learning approach is demonstrated. The ensemble learning
framework used in this study consists of random sampling with replacement from the
training dataset, the construction of several predictive models (weak learners), and
the combination of their outputs. This approach enables us to obtain a reasonable
prediction model that avoids the problem of overfitting, even when analyzing a small
dataset.
Key words
machine learning - catalytic reactions - reaction rates - ensemble learning - small
datasets
