# Classification of datasets with imputed missing values: does imputation quality matter?

arXiv: https://arxiv.org/abs/2206.08478 · HTML: https://mlsanity.com/paper/2206.08478/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 85 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Importance Weighted Autoencoders](https://mlsanity.com/paper/1509.00519/) (2015)
- [New insights and perspectives on the natural gradient method](https://mlsanity.com/paper/1412.1193/) (2014)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III](https://mlsanity.com/paper/1907.08322/) (2019)
- [Missing Data Imputation using Optimal Transport](https://mlsanity.com/paper/2002.03860/) (2020)
- [NGBoost: Natural Gradient Boosting for Probabilistic Prediction](https://mlsanity.com/paper/1910.03225/) (2019)
- [An Empirical Comparison of Multiple Imputation Methods for Categorical Data](https://mlsanity.com/paper/1508.05918/) (2015)
- [Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison](https://mlsanity.com/paper/2103.09316/) (2021)
- [Missing Data Imputation for Classification Problems](https://mlsanity.com/paper/2002.10709/) (2020)

## Built on by (most cited first, 22 of 22)

- [Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models](https://mlsanity.com/paper/2208.09399/) (2022)
- [REFORMS: Reporting Standards for Machine Learning Based Science](https://mlsanity.com/paper/2308.07832/) (2023)
- [Imputation for prediction: beware of diminishing returns](https://mlsanity.com/paper/2407.19804/) (2024)
- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [Interpretable Generalized Additive Models for Datasets with Missing Values](https://mlsanity.com/paper/2412.02646/) (2024)
- [Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series](https://mlsanity.com/paper/2409.09930/) (2024)
- [Leveraging Predictive Equivalence in Decision Trees](https://mlsanity.com/paper/2506.14143/) (2025)
- [Risk-based Calibration for Generative Classifiers](https://mlsanity.com/paper/2409.03542/) (2024)
- [Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation](https://mlsanity.com/paper/2409.07510/) (2024)
- [Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation](https://mlsanity.com/paper/2606.03517/) (2026)
- [HyperMM : Robust Multimodal Learning with Varying-sized Inputs](https://mlsanity.com/paper/2407.20768/) (2024)
- [To impute or not to impute: How machine learning modelers treat missing data](https://mlsanity.com/paper/2503.16644/) (2025)
- [Prediction Models That Learn to Avoid Missing Values](https://mlsanity.com/paper/2505.03393/) (2025)
- [Decentralized Federated Learning of Probabilistic Generative Classifiers](https://mlsanity.com/paper/2507.17285/) (2025)
- [No Modality Left Behind: Dynamic Model Generation for Incomplete Medical Data](https://mlsanity.com/paper/2509.11406/) (2025)
- [Imputation Uncertainty in Interpretable Machine Learning Methods](https://mlsanity.com/paper/2512.17689/) (2025)
- [Drop the mask! GAMM-A Taxonomy for Graph Attributes Missing Mechanisms](https://mlsanity.com/paper/2602.08407/) (2026)
- [Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modeling](https://mlsanity.com/paper/2602.16979/) (2026)
- [tBayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Data](https://mlsanity.com/paper/2603.27142/) (2026)
- [Predicting missing values: A good idea?](https://mlsanity.com/paper/2605.03733/) (2026)
- [Novel Dynamic Batch-Sensitive Adam Optimiser for Vehicular Accident Injury Severity Prediction](https://mlsanity.com/paper/2605.15083/) (2026)
- [When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch](https://mlsanity.com/paper/2610.20342/) (2026)

## Abstract (arXiv)

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data.