Grouping Shapley Value Feature Importances of Random Forests for explainable Yield Prediction. (arXiv:2304.07111v1 [cs.LG])

Explainability in yield prediction helps us fully explore the potential of
machine learning models that are already able to achieve high accuracy for a
variety of yield prediction scenarios. The data included for the prediction of
yields are intricate and the models are often difficult to understand. However,
understanding the models can be simplified by using natural groupings of the
input features. Grouping can be achieved, for example, by the time the features
are captured or by the sensor used …

accuracy art arxiv data example explainability feature features machine machine learning machine learning models natural prediction random random forests sensor simplified state understanding value