Systematic assessment of mixed imputation methods and explainable machine learning

Figure 1 Schematic representation of the HDI-MF-Gower hybrid imputation framework. A: Warm start initialization: Raw data containing missing values (white blocks) are processed using an adaptive weighted Gower distance. Unlike standard mean imputation, this step assigns dynamic weights (w_k) to variables based on their variance and missing rates, ensuring that high-information donors drive the initial “guess” (light blue blocks); B: Iterative optimization: The initialized matrix enters a MissForest loop. For each variable, a random forest regressor/classifier captures non-linear interactions and updates the missing entries. The cycle repeats until the difference (δ) between consecutive iterations falls below the convergence threshold (γ), yielding the final imputed dataset.