MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909726421286912 |
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| author | Zhou, Youran Bouadjenek, Mohamed Reda Aryal, Sunil |
| author_facet | Zhou, Youran Bouadjenek, Mohamed Reda Aryal, Sunil |
| contents | Incomplete data is a persistent challenge in real-world datasets, often governed by complex and unobservable missing mechanisms. Simulating missingness has become a standard approach for understanding its impact on learning and analysis. However, existing tools are fragmented, mechanism-limited, and typically focus only on numerical variables, overlooking the heterogeneous nature of real-world tabular data. We present MissMecha, an open-source Python toolkit for simulating, visualizing, and evaluating missing data under MCAR, MAR, and MNAR assumptions. MissMecha supports both numerical and categorical features, enabling mechanism-aware studies across mixed-type tabular datasets. It includes visual diagnostics, MCAR testing utilities, and type-aware imputation evaluation metrics. Designed to support data quality research, benchmarking, and education,MissMecha offers a unified platform for researchers and practitioners working with incomplete data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04740 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms Zhou, Youran Bouadjenek, Mohamed Reda Aryal, Sunil Machine Learning Mathematical Software Incomplete data is a persistent challenge in real-world datasets, often governed by complex and unobservable missing mechanisms. Simulating missingness has become a standard approach for understanding its impact on learning and analysis. However, existing tools are fragmented, mechanism-limited, and typically focus only on numerical variables, overlooking the heterogeneous nature of real-world tabular data. We present MissMecha, an open-source Python toolkit for simulating, visualizing, and evaluating missing data under MCAR, MAR, and MNAR assumptions. MissMecha supports both numerical and categorical features, enabling mechanism-aware studies across mixed-type tabular datasets. It includes visual diagnostics, MCAR testing utilities, and type-aware imputation evaluation metrics. Designed to support data quality research, benchmarking, and education,MissMecha offers a unified platform for researchers and practitioners working with incomplete data. |
| title | MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms |
| topic | Machine Learning Mathematical Software |
| url | https://arxiv.org/abs/2508.04740 |