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 to do so. The state-of-the-art for interpreting machine learning models is currently defined by the game-theoretic approach of Shapley values. To handle groups of features, the calculated Shapley values are typically added together, ignoring the theoretical limitations of this approach. We explain the concept of Shapley values directly computed for predefined groups of features and introduce an algorithm to compute them efficiently on tree structures. We provide a blueprint for designing swarm plots that combine many local explanations for global understanding. Extensive evaluation of two different yield prediction problems shows the worth of our approach and demonstrates how we can enable a better understanding of yield prediction models in the future, ultimately leading to mutual enrichment of research and application.

翻译：产量预测中的可解释性有助于我们充分发掘机器学习模型的潜力，这些模型已在多种产量预测场景中实现高精度。用于产量预测的数据复杂且模型往往难以理解。然而，通过利用输入特征的自然分组可以简化对模型的理解。分组可依据特征的采集时间或所用传感器等方式实现。当前解释机器学习模型的前沿方法由博弈论框架的沙普利值定义。处理特征组时，通常将计算所得沙普利值简单相加，但这一做法忽略了该方法的理论局限性。我们阐释了直接为预定义特征组计算的沙普利值概念，并引入一种在树结构上高效计算该值的算法。我们提供了设计群图（swarm plots）的蓝图，该图可融合多个局部解释以实现全局理解。对两个不同产量预测问题的广泛评估证明了我们方法的有效性，并展示了未来如何更好地理解产量预测模型，最终实现研究与应用的相互促进。