What Interpretability Meaning, Applications & Example
The ability to explain a model's predictions in understandable terms.
What is Interpretability?
Interpretability in machine learning refers to the ability to understand and explain how a model makes its predictions or decisions. An interpretable model allows humans to gain insights into the relationships between input features and the model’s output, making it easier to trust and validate the model’s behavior. Interpretability is especially important in high-stakes fields such as healthcare, finance, and law, where understanding the reasoning behind a model’s decision is critical.
Types of Interpretability
- Model Interpretability: This is inherent to certain models that are simple and easy to interpret, such as decision trees, linear regression, or logistic regression . These models have a clear structure that can be easily understood by humans.
- Post-hoc Interpretability: For more complex models like deep neural networks or ensemble methods, interpretability techniques are applied after the model has been trained. These techniques attempt to explain the decisions of a model that is not inherently interpretable.
- Global Interpretability: This refers to understanding the overall behavior of the model across the entire dataset, such as how features influence predictions on average.
- Local Interpretability: This focuses on understanding individual predictions made by the model, explaining why a particular decision was made for a specific input.
Methods for Improving Interpretability
- Feature Importance: Identifying which input features are most influential in the model’s decision-making process, such as through techniques like permutation importance or SHAP (Shapley Additive Explanations) values.
- Surrogate Models: Using simpler, interpretable models (like decision trees or linear models) to approximate the behavior of complex models in a specific region of the input space.
- Partial Dependence Plots (PDP): Visualizing the relationship between one or more input features and the predicted outcome, helping to understand how features impact predictions.
- LIME (Local Interpretable Model-agnostic Explanations): A technique that approximates a complex model with a simpler, interpretable model for a specific prediction to explain the model’s behavior locally.
Applications of Interpretability
- Healthcare: In medical diagnostics, interpretability helps healthcare professionals trust machine learning models for decisions like diagnosis or treatment recommendations.
- Finance: In credit scoring, interpretability is important to explain why a loan application was approved or denied, ensuring fairness and transparency .
- Legal: In judicial systems, machine learning models used in sentencing or parole decisions must be interpretable to ensure compliance with legal and ethical standards.
Example of Interpretability
An example of interpretability can be seen in a decision tree used for classifying whether a loan applicant should be approved or denied. The decision tree might use input features such as credit score, income, and debt-to-income ratio. By tracing the path from the root to the leaf node, you can easily see how these features influence the final decision, making it straightforward for a loan officer to understand why an applicant was approved or rejected.