# Synthesizing Explainability Across Multiple ML Models for Structured Data
## 5 Discussion
Thus, even when correlated variables share predictive capacity, their collective inclusion in WISFC contributes to a more comprehensive feature list that remains actionable for explanatory and decision-support purposes. Aggregating importance across models allows such features, whether independent or partially redundant, to be flagged for further clinical or domain-specific evaluation, which is often the practical objective in many structured-data applications. Computationally, WISFC adds negligible overhead: the primary cost lies in training multiple models. For small to medium datasets, brute-force ensembles are practical, and deriving WIS and FC from existing importance lists is trivial. Even on larger datasets, practitioners can limit the ensemble to key model variants or use cross-validation folds as proxies. In all cases, the modest computational investment yields far greater clarity and robustness in feature interpretation. For **practitioners** and **researchers** looking to apply WISFC, here are a few **guidelines and best practices**:  
* **Assemble a diverse ensemble**. Include both simple (e.g., linear) and complex (e.g., tree-based, gradient boosting) models. Ensure each model is well-tuned so that importance differences reflect genuine explanatory variance, not poor fit. * **Normalize importances**. Convert all feature-importance outputs to a common scale; rank-transformations are robust. Even SHAP values, which share units, can benefit from normalization to prevent scale discrepancies. * **Select a sensible top-k for FC**. Choose k to reflect the expected number of relevant features (e.g., k = 10 for ten predictors). Avoid k so small that valid features hover just outside the cutoff or so large that FC loses discrimination. Test multiple k values; true robust features maintain high FC across reasonable ranges. * **Apply performance-based weighting judiciously**. If one model significantly outperforms others on validation metrics, weight its importances more heavily. If model performances are comparable, use equal weights to avoid overemphasis. Always base weights on held-out or cross-validated scores to guard against overfitting. * **Interpret WIS and FC in context**. * **High WIS and high FC**: robust predictors--report with confidence. * **High WIS and low FC**: model-specific signals--investigate further. Low WIS and high FC: consistently moderate signals--consider for theory or follow-up testing. By following these steps, practitioners can leverage WISFC to derive reliable, transparent insights from any structured dataset. The introduction of WISFC carries some broader implications for explainable AI research and practice. It encourages a mindset shift from focusing on one model, one explanation, to multiple models, one synthesized explanation. This resonates with the general ensemble philosophy in ML--just as ensemble predictions are more reliable, ensemble explanations can be more trustworthy. It may prompt tool builders to incorporate features for explanation aggregation (e.g., an AutoML system could produce not just an ensemble model but an ensemble feature-importance report using something like WISFC). For high-stakes decision-making, using WISFC could become a best practice--for instance, regulatory guidelines on ML in healthcare might require not only model performance metrics but also some assessment of explanation robustness, as already suggested in early intelligible ML work in healthcare [21]; WISFC could serve as part of that assessment, signaling that certain conclusions are not model-dependent. The method also opens up several research avenues. One could extend WISFC to local explanations (aggregating explanations for individual predictions across multiple models to see if they agree on why a specific prediction was made--somewhat like AGREE's approach [14], but focusing on agreement for a given instance rather than global features).