Missing Data Imputation for Supervised Learning
نویسندگان
چکیده
منابع مشابه
Missing Data Imputation for Supervised Learning
This paper compares methods for imputing missing categorical data for supervised learning tasks. The ability of researchers to accurately fit a model and yield unbiased estimates may be compromised by missing data, which are prevalent in survey-based social science research. We experiment on two machine learning benchmark datasets with missing categorical data, comparing classifiers trained on ...
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ژورنال
عنوان ژورنال: Applied Artificial Intelligence
سال: 2018
ISSN: 0883-9514,1087-6545
DOI: 10.1080/08839514.2018.1448143