  The percolation threshold is an important measure to determine the inherent
rigidity of large networks. Predictors of the percolation threshold for large
networks are computationally intense to run, hence it is a necessity to develop
predictors of the percolation threshold of networks, that do not rely on
numerical simulations. We demonstrate the efficacy of five machine
learning-based regression techniques for the accurate prediction of the
percolation threshold. The dataset generated to train the machine learning
models contains a total of 777 real and synthetic networks. It consists of 5
statistical and structural properties of networks as features and the
numerically computed percolation threshold as the output attribute. We
establish that the machine learning models outperform three existing empirical
estimators of bond percolation threshold, and extend this experiment to predict
site and explosive percolation. Further, we compared the performance of our
models in predicting the percolation threshold using RMSE values. The gradient
boosting regressor, multilayer perceptron and random forests regression models
achieve the least RMSE values among considered models.
