2020/07/06 by Katarzyna Woźnica, Woźnica, Katarzyna, Przemysław Biecek +1 · 13 citations
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Benchmark (surveying) #Cartography #Computer science #Econometrics #FOS: Computer and information sciences #Geography #Imputation (statistics) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Mathematics #Missing data #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2007.02837
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/07/06 · openalex publication_date 2020/07/06 · arxiv updated 2020/07/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Incomplete data are common in practical applications. Most predictive machine learning models do not handle missing values so they require some preprocessing. Although many algorithms are used for data imputation, we do not understand the impact of the different methods on the predictive models' performance. This paper is first that systematically evaluates the empirical effectiveness of data imputation algorithms for predictive models. The main contributions are (1) the recommendation of a general method for empirical benchmarking based on real-life classification tasks and the (2) comparative analysis of different imputation methods for a collection of data sets and a collection of ML algorithms.