With increasing interest in explaining machine learning (ML) models, the
first part of this two-part study synthesizes recent research on methods for
explaining global and local aspects of ML models. This study distinguishes
explainability from interpretability, local from global explainability, and
feature importance versus feature relevance. We demonstrate and visualize
different explanation methods, how to interpret them, and provide a complete
Python package (scikit-explain) to allow future researchers to explore these
products. We also highlight the frequent disagreement between explanation
methods for feature rankings and feature effects and provide practical advice
for dealing with these disagreements. We used ML models developed for severe
weather prediction and sub-freezing road surface temperature prediction to
generalize the behavior of the different explanation methods. For feature
rankings, there is substantially more agreement on the set of top features
(e.g., on average, two methods agree on 6 of the top 10 features) than on
specific rankings (on average, two methods only agree on the ranks of 2-3
features in the set of top 10 features). On the other hand, two feature effect
curves from different methods are in high agreement as long as the phase space
is well sampled. Finally, a lesser-known method, tree interpreter, was found
comparable to SHAP for feature effects, and with the widespread use of random
forests in geosciences and computational ease of tree interpreter, we recommend
it be explored in future research.

这篇论文探讨了机器学习模型的可解释性和可解释性，特别是关于本地和全局可解释性以及特征重要性和相关性的方法，并提供了一个完整的 Python 软件包来允许未来的研究人员探索这些产品；根据对严重天气预测和亚冰点路面温度预测的 ML 模型开发的研究， 发现有关特征排名和特征效果的解释方法之间存在显着不一致，并提供建议来处理这些分歧；最后，建议在未来的研究中使用 tree interpreter 方法来探究特征效果。