Abstract
Binning is a process to group number of more or less continuous values into a smaller number of ‘bins’ based on certain criteria which results in handling high volume of data in computationally less expensive time. By adopting the process of binning we basically reduce the cardinality of the data. This paper proposes machine learning-based robust model which can work upon a high cardinality data-set and reproduce bins.
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Nanda, M.B., Mishra, B.S.P., Anand, V. (2022). Effects of Binning on Logistic Regression-Based Predicted CTR Models. In: Dehuri, S., Prasad Mishra, B.S., Mallick, P.K., Cho, SB. (eds) Biologically Inspired Techniques in Many Criteria Decision Making. Smart Innovation, Systems and Technologies, vol 271. Springer, Singapore. https://doi.org/10.1007/978-981-16-8739-6_42
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DOI: https://doi.org/10.1007/978-981-16-8739-6_42
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