Machine learning models are increasingly used in critical areas like healthcare and finance, but their decision-making processes often remain unclear. While tools like SHAP and LIME offer some insights, they don't provide a straightforward way to measure exactly how much each input feature affects the model's predictions. This makes it difficult to audit models, explain decisions to stakeholders, or systematically improve performance.

A New Way to Measure Feature Impact

One approach could be to develop a standardized method that assigns a measurable "effect size" to each input feature in a classifier. Unlike existing techniques that show relative importance, this would answer questions like:

• How much does a specific increase in income change loan approval odds?
• Which medical factor contributes most to a diagnosis, and by what exact amount?

The method might work by defining a universal metric (like prediction probability change per unit input), designing algorithms that work across different model types, and creating visualizations that make the results clear to non-technical users.

Practical Applications and Implementation

Such a tool could serve multiple stakeholders:

• Data scientists could use it to debug models and identify biases
• Doctors or loan officers could better understand automated decisions
• Regulators could verify model fairness using quantifiable metrics

A simple starting point might be a Python library that computes these effect sizes for common models, with basic visualization capabilities. After validating the approach on public datasets, support could be expanded to deep learning frameworks and domain-specific applications.

Advantages Over Existing Methods

Compared to current interpretation tools, this approach would offer:

Clearer decision-making support: Instead of just showing which features matter most, it would quantify exactly how much they matter - crucial for real-world applications where stakeholders need to understand the practical impact of changing specific inputs.

Better regulatory compliance: The numeric effect measures could help meet requirements like GDPR's "right to explanation" more effectively than relative importance scores.

While challenges like handling feature interactions remain, this approach could provide a more actionable way to understand and trust machine learning decisions across industries.