Random Forest for Histogram Data: An application in data-driven prognostic models for heavy-duty trucks

This thesis investigates how the standard decision tree and random forest algorithms can be adapted to make them learn more accurate models from histogram data, and evaluates an interactive visual tool for assisting users to understand the reasons behind the output of the models.