A Machine Learning Method for Parameter Estimation and Sensitivity Analysis.

We discuss the application of a supervised machine learning method, random forest algorithm (RF), to perform parameter space exploration and sensitivity analysis on ordinary differential equation models. Decision trees can provide complex decision boundaries and can help visualize decision rules in an easily digested format that can aid in understanding the predictive structure of a dynamic model and the relationship between input parameters and model output. We study a simplified process for model parameter tuning and sensitivity analysis that can be used in the early stages of model development.