# Synthesizing Explainability Across Multiple ML Models for Structured Data
## 5 Discussion  
Traditional methods for merging multiple model explanations, such as comparing top-k overlaps or defaulting to a single model's output, often rely on informal judgments and can produce inconsistent or misleading results. WISFC overcomes these limitations by introducing two reproducible metrics: Weighted Importance Score, which quantifies a feature's average strength, and Frequency Count, which measures its consistency across models. Together, these metrics provide clear, numeric justification for why certain features are deemed robust. Applying WISFC to small, noisy datasets demonstrates that training a diverse ensemble of models uncovers a broader set of plausible predictors than any individual model alone. Features that appear only sporadically, like a genetic marker flagged by one algorithm, are retained but receive a low-frequency score, signaling caution. Conversely, predictors that consistently rank well, even if never top, are elevated as reliable signals. This ensemble-driven approach rescues true positives (for example, BMI or cholesterol in our clinical case) that might otherwise be overlooked. Several key benefits emerge:  
* Rigor and Reproducibility: Clear WIS and FC values replace subjective overlap heuristics, enabling others to replicate the aggregation and even apply statistical tests to these metrics. * Domain-Agnostic Flexibility: Any model type and importance measure can feed into WISFC, making it equally suited to healthcare, marketing analytics, or engineering diagnostics. * Robustness to Noise: By combining magnitude and consensus, WISFC down-weights one-off, model-specific quirks, and highlights features likely to generalize beyond the training data. * Stable Conclusions: Minor changes, such as retraining one model, have a limited impact on high-frequency features, avoiding brittle exclusions inherent in strict intersection rules. In sum, WISFC delivers a balanced, transparent framework for reliable knowledge discovery from structured data using multiple models. WISFC excels at combining diverse model insights into a single, transparent feature ranking, but its effectiveness depends on the underlying models and data quality. When the ensemble consists of only minor variations of the same algorithm (e.g., different random seeds), WISFC will boost stability, as expected from any ensemble, but add little new information beyond conventional uncertainty intervals. To maximize its value, include genuinely diverse model types or architectures (linear vs. non-linear, tree-based vs. kernel methods, etc.). WISFC also faithfully aggregates whatever biases are present. If all models inherit the same data skew or flawed feature engineering, a spurious signal may receive high WIS and FC. Rigorous data preparation, thoughtful feature selection, and domain-expert sanity checks remain essential. Highly correlated or redundant predictors present another challenge. Different models may alternate between correlated features X and Y, resulting in moderate WIS and reduced FC for both, even though the underlying signal is strong. In such cases, interpreters should recognize feature groups rather than isolated variables. Clustering correlated features before aggregation is one extension, but at minimum, analysts should consider collective importance when interpreting WISFC rankings. While correlated predictors may distribute importance across multiple features, we emphasize that, unlike traditional regression models where multicollinearity directly compromises coefficient estimates, ML models are primarily designed for prediction rather than strict causal inference. However, predictive modeling can still serve as a valuable basis for explanation. Prediction and explanation are not opposing paradigms but can complement each other [20], by capturing different aspects of the data-generating process.