What is Interpretability?

Learn how an algorithm is deemed to be interpretable, and how it is different to being explainable

What does it mean to be interpretable?

Models are interpretable when humans can readily understand the reasoning behind predictions and decisions made by the model.

The more interpretable the models are, the easier it is for someone to comprehend and trust the model.

Models such as deep learning and gradient boosting are not interpretable and are referred to as black-box models because they are too complex for human understanding. It is impossible for a human to comprehend the entire model at once and understand the reasoning behind each decision.

Examples of interpretable models

There are many traditional classes of models that are easily interpretable, such as linear regression and decision tree models.

Interpretability is not simply a binary determination, as it can also depend on the complexity of the particular model in question. For instance, a linear regression that uses five features is significantly more interpretable than one using 100 features.

Illustrative decision tree for marketing decisions in investment management. The logic of the tree is clear and transparent.

How is it different from model explainability?

Explainable methods such as LIME and SHAP give some peek into a trained black-box model, providing post-hoc explanation for particular outputs.

Compared to natively interpretable models, these explainable tools cannot give a global view of the model holistically and accurately. We compare and contrast the approaches in a case study on predicting risk of loan default.

Example of SHAP output for predicting risk of loan default

Overcoming the price of interpretability

Standard approaches for creating interpretable models are typically outperformed by black boxes. This creates a tradeoff between model accuracy and interpretability.

Our proprietary algorithms generate interpretable models that reach the same level of performance as black-box methods, demonstrated by extensive benchmark studies published in the top academic journals.

A large-scale benchmark study demonstrates that the performance of our Optimal Decision Trees (OCT-H and OCT) is competitive with black-box methods (XGBoost and Random Forest)

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