Shapley additive explanations for NO2 forecasting

By M. García, J. Aznarte. Ecological Informatics, 2020

312 citations recorded by the source corpus.

Abstract In this paper, we address the problem of the interpretability of a machine learning model designed to predict air quality time series. When constructing a forecasting model, in addition to obtaining good accuracy, it is utterly important to understand why each prediction is made. Usually, interpreting the output of machine learning models is considered to be very difficult due to their complex “black box” architecture. However, we show how Shapley additive explanations can be used to interpret the outputs of a deep neural network designed to predict Nitrogen dioxide concentrations in Madrid. This method computes an estimation of the contribution of each feature for a particular prediction. Furthermore, we compare three explanatory methods to determine which one is more suitable for the air quality data and for the chosen machine learning model. A deeper insight into how the model behaves when predicting the pollution time series is obtained.

Subfields: Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/8283d4e2508c9a250b84118bacde9d0014711796

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