Machine learning (ML) methods are becoming popular tools for the prediction
and design of novel materials. In particular, neural network (NN) is a
promising ML method, which can be used to identify hidden trends in the data.
However, these methods rely on large datasets and often exhibit overfitting
when used with sparse dataset. Further, assessing the uncertainty in
predictions for a new dataset or an extrapolation of the present dataset is
challenging. Herein, using Gaussian process regression (GPR), we predict
Young's modulus for silicate glasses having sparse dataset. We show that GPR
significantly outperforms NN for sparse dataset, while ensuring no overfitting.
Further, thanks to the nonparametric nature, GPR provides quantitative bounds
for the reliability of predictions while extrapolating. Overall, GPR presents
an advanced ML methodology for accelerating the development of novel functional
materials such as glasses.