Researchers have introduced ImputeViz, an integrated visual analytics dashboard designed to support the diagnosis of missingness patterns, configuration of imputation models, and evaluation of results. The system integrates widely used methods such as MICE, Random Forest, XGBoost, and kNN within an interactive environment that makes missingness structures explicit.
- The tool introduces gKNN, a geographically informed kNN variant that blends socioeconomic and spatial distances to enable provenance-based visual accountability.
- Coordinated views reveal missingness structure through heatmaps, co-missingness summaries, and distributional diagnostics to help analysts reason about MCAR, MAR, and MNAR patterns.
- Users can compare and tune models via distributional overlays and a Method Comparison Summary reporting MAE, RMSE, Delta RMSE, and runtime for each algorithm.
- Cached per-method results and locked axis scales reduce cognitive overhead from shifting ranges during method switching.
Case studies demonstrate how ImputeViz helps analysts select effective strategies, surface sensitive variables, and assess model robustness.