Abstract
Measuring the influence of particular data point is an important task in designing a data hub. In recent years, several research works have addressed the problem. In this paper we analyze these approaches and show that they are too complicated to apply in practice. We propose a new lightweight approach to approximate the influence of data points. We evaluate our proposal on the popular Home Credit dataset to show the effective.
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Acknowledgements
My-Linh Tran, VINIF.2020.ThS.QN.02 was funded by Vingroup Joint Stock Company and supported by the Domestic Master/Ph.D. Scholarship Programme of Vingroup Innovation Foundation (VINIF), Vingroup Big Data Institute (VinBigdata).
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Dang, QV. et al. (2022). Group’s Influence Value in Logistic Regression Model and Gradient Boosting Model. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_66
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DOI: https://doi.org/10.1007/978-981-16-2377-6_66
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