Interpretable Machine Learning—Tools to Interpret the Predictions of a Machine Learning Model Predicting the Electrical Energy Consumption of an Electric Arc Furnace,Steel Research International

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Interpretable Machine Learning—Tools to Interpret the Predictions of a Machine Learning Model Predicting the Electrical Energy Consumption of an Electric Arc Furnace
Steel Research International ( IF 1.9 ) Pub Date : 2020-03-11 , DOI: 10.1002/srin.202000053
Leo S. Carlsson ₁ , Peter B. Samuelsson ₁ , Pär G. Jönsson ₁

Affiliation

Machine learning (ML) is a promising modeling framework that has previously been used in the context of optimizing steel processes. However, many of the more advanced ML models, capable of providing more accurate predictions to complex problems, are often impossible to interpret. This makes the domain experts in the steel industry, to a large extent, hesitant to adopt these models. The valuable increase in model accuracy is diminished by the lack of model interpretability. Herein, Shapley additive explanations (SHAP) is applied to an advanced ML model, predicting the electrical energy (EE) consumption of an electric arc furnace (EAF). The insights from SHAP reveal the contributions from each input variable on the EE for every single heat in the prediction domain. These contributions are then evaluated based on process metallurgical experience.

中文翻译：

可解释性机器学习-解释预测电弧炉电能消耗的机器学习模型预测的工具

机器学习（ML）是一个很有前途的建模框架，该框架先前已用于优化钢工艺的环境中。但是，许多更高级的ML模型能够为复杂的问题提供更准确的预测，通常无法解释。这使得钢铁行业的领域专家在很大程度上不愿采用这些模型。缺乏模型可解释性，从而减少了模型准确性的宝贵提高。此处，将Shapley添加剂说明（SHAP）应用于高级ML模型，以预测电弧炉（EAF）的电能（EE）消耗。SHAP的见解揭示了EE中每个输入变量对预测域中每个热量的贡献。然后根据过程冶金经验评估这些贡献。

更新日期：2020-03-11

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