Model Interpretability and Explainability

The Complete Model Interpretability and Explainability Guide 2

category: AI in Finance and FinTech

tags: ['machine learning', 'AI algorithms', 'deep learning', 'neural networks', 'data science', 'ML models', 'artificial intelligence', 'predictive analytics']

Quantifying Feature Importance Isn’t a Simple Task. Various Techniques

Permutation Importance: This is arguably the most widely used method for assessing feature importance in deep learning. It operates on the principle that if a feature has a significant impact on model performance, randomly shuffling its values will drastically degrade prediction accuracy.

The greater the decrease in accuracy after shuffling, the higher the feature’s importance. Mathematically, it measures the reduction in predictive power caused by permuting each feature.

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LIME (Local Interpretable Model-Agnostic Explanations): Approximates t

Counterfactual Explanations: Generates examples that highlight how changes to input features would alter the prediction - useful for understanding what conditions would be needed to achieve a desired outcome. For instance, in loan applications, it can reveal the factors that need to change (e.g., income or credit score) to get approved.

Partial Dependence Plots: Visualizes the relationship between one or two input features and the predicted output – useful for understanding how features interact with each other.

Frequently asked questions

What is Permutation Importance in the context of model interpretability?

Permutation Importance assesses feature importance by randomly shuffling a feature's values and measuring the resulting decrease in model accuracy. The greater the accuracy drop, the more important that feature is.

What is LIME used for in explaining AI models?

LIME (Local Interpretable Model-Agnostic Explanations) provides local explanations of model predictions by creating simplified, interpretable models around a specific prediction. It's particularly useful for understanding why a model made a particular decision.

What is a Counterfactual Explanation?

A counterfactual explanation describes the minimal changes to an input that would be needed to flip a predicted outcome, helping users understand what factors influence the model’s decisions.

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