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References

1 
A. Adadi, M. Berrada, 2018, Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI), IEEE Access, Vol. 6, pp. 52138-52160DOI
2 
C. Molnar, 2019, Interpretable Machine Learning: A Guide to Making Black Box Models Explainable, LeanpubGoogle Search
3 
K. Lee, W.-J. Kim, 2016, Forecasting of 24_hours Ahead Photovoltaic Power Output Using Support Vector Regres- sion, The Journal of Korean Institute of Information Technology, Vol. 14, No. 3, pp. 175-183Google Search
4 
D.-H. Shin, J.-H. Park, C.-B. Kim, 2017, Photovoltaic Generation Forecasting Using Weather Forecast and Predictive Sun- shine and Radiation, Journal of Advanced Navigation Technology, Vol. 21, No. 6, pp. 643-650DOI
5 
Y. Lee, 2019, Validation of Forecasting Performance of Two-stage Probabilistic Solar Irradiation and Solar Power Forecasting Algorithm Using XGBoost, The Transactions of the Korean Institute of Electrical Engineers, Vol. 68, No. 12, pp. 1704-1710Google Search
6 
G. Chandrashekar, F. Sahin, 2014, A Survey on Feature Selection Methods, Computers & Electrical Engineering, Vol. 40, No. issue 1, pp. 16-28DOI
7 
T. Chen, C. Guestrin, 2016, XGBoost: A Scalable Tree Boosting System, KDDDOI
8 
S. Lundberg, S.-I. Lee, Nov 2017, A Unified Approach to Inter- preting Model Predictions, NIPSGoogle Search
9 
S. Lundberg, G. Erion, S.-I. Lee, 2019, Consistent Indivi- dualized Feature Attribution for Tree Ensembles, arXiv: 1802.03888Google Search
10 
https://github.com/slundberg/shap,