Abstract
A straightforward approach to handling missing values is dropping incomplete records from the dataset. However, for many forms of missingness, this method is known to affect the center and spread of the data distribution. In this paper, we perform an extensive empirical evaluation of the effect of the drop method on the data distribution. In particular, we analyze two scenarios that are likely to occur in practice but are not often considered in simulation studies: 1) when features are skewed rather than symmetrically distributed and 2) when multiple forms of missingness occur simultaneously in one feature. Furthermore, we investigate implications of the drop method for classification accuracy and demonstrate that dropping incomplete records is doubtful, even when test cases are dropped as well.
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Notes
- 1.
- 2.
N.B.: in the general case, this may affect training and test distribution, but it is unclear how. Homogeneity might increase, but the data might also become more scattered and hence variance might increase. Since the distribution can be affected in a wide variety of possible ways, we will simply ignore this effect; note that technically this might affect the definition of accuracy.
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Acknowledgments
Many thanks to dr. Wouter Duivesteijn and prof. Mykola Pechenizkiy for their continuous support in all possible ways. Thank you Hilde Weerts for being a sparring partner.
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Schouten, R.M., Taşcău, V., Ziegler, G.G., Casano, D., Ardizzone, M., Erotokritou, MA. (2023). Dropping Incomplete Records is (not so) Straightforward. In: Crémilleux, B., Hess, S., Nijssen, S. (eds) Advances in Intelligent Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science, vol 13876. Springer, Cham. https://doi.org/10.1007/978-3-031-30047-9_30
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