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Application of interpretable machine learning models for the intelligent decision

Yawen Li,Liu Yang,Bohan Yang,Ning Wang,Tian Wu

Published 2019 in Neurocomputing

ABSTRACT

Abstract In this study, an interpretable machine learning algorithm is proposed for the issues of intelligent decision through predicting the firms’ efficiency of innovation. Based on the unbalanced panel data collected in Zhongguancun Science Parks from year 2005 to 2015, the efficiency of over 10,000 firms have been analysed in this study, and the change and growth of these firms have been captured over time. The linear regression, decision tree, random forests, neural network and XGBoost models are applied to figure out the impact factors of innovation. After comparing the results of different models, it has been found that the accuracy of XGBoost for R&D efficiency labelled, commercial efficiency labelled and overall efficiency labelled classification problems are 73.65%, 70.02% and 70.09%, which outperform the other four models. Moreover, the interpretability of XGBoost is also better than other models. Thus, the XGBoost model makes it possible for managers to predict the firm's future innovation performance derived from their innovation strategies in the current stage. Furthermore, it helps firms to build an intelligent decision support system, which is of great importance for them to deal with complex decision environments, and to increase their efficiency of innovation in the long-term dynamic competition with other firms.

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