Partial dependence plots for inspecting machine learning models of sugarcane yield
Sugarcane yield models are important tools for planning purposes in the sucroenergetic sector. When black-box techniques are used to create such models, methodologies such as partial dependence plots are required for further understanding them. We evaluated partial dependence plots for a few selected important variables. We observed that different techniques learned similar responses. The patterns were consistent across different techniques, feature sets, and the use of feature selection.