IMBALANCE OF CLASSES IN SOLVING THE PROBLEM OF SOCIAL NETWORKS USER CLASSIFICATION FOR PROFESSIONAL ORIENTATION

V. Obrubova, M. Ozerova
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Abstract

The problem of data imbalance is often underestimated when solving classification problems. A classification model that looks well trained on your data and gives a good recognition rate may not be reliable. Consideration of this problem in the specific task of classifying users of social networks will make it possible to understand how, why and, most importantly, when it is necessary to get rid from data imbalances.
类的不平衡在解决社交网络用户分类问题上的专业定位
在解决分类问题时,数据不平衡的问题往往被低估。如果一个分类模型在你的数据上训练得很好,并且识别率很高,那么它可能并不可靠。在对社交网络用户进行分类的具体任务中考虑这个问题,将有可能理解如何,为什么,最重要的是,何时有必要摆脱数据不平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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