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. They also reflected knowledge about the crop.