Model Interpretability

Model interpretability is the ability to understand or explain how a model performs predictions. It indicates whether a model’s decisions can be explained to humans and how well the reasoning behind them can be communicated.

Interpretability by Model Type[edit | edit source]

The following list generally starts with models that are considered to have higher interpretability.

Linear Regression[edit | edit source]

A simple mathematical model that assigns linear coefficients to each feature, allowing for easy interpretation of each feature's impact on the target variable.
- For instance, a linear regression model for predicting annual income might look like this, where the coefficients provide insights into each feature's influence:
  - e.g., Annual Income = 2500 + (4000 × Education Level) + (200 × Years of Experience) + (10000 × Job Position) + (300 × Performance Score) + (50 × Weekly Work Hours)
Positive coefficients indicate a positive impact, while negative coefficients indicate a negative impact.

Logistic Regression[edit | edit source]

Used for binary classification, it allows users to understand the impact of each feature on the outcome (whether it is more likely to lead to a positive result) by interpreting the coefficients, similar to linear regression.

Decision Tree[edit | edit source]

The branching conditions within the tree visually indicate which features contribute to predictions.
If the tree is not too deep, the entire model can be represented graphically, making it easy to understand.
However, when trees become very complex, analyzing feature impact can become increasingly difficult.

Naive Bayes Classifier[edit | edit source]

Assumes independence among features, performing predictions probabilistically. The influence of each feature can be described through conditional probabilities, making it relatively easy to interpret.

Differences from Explainable AI (XAI)[edit | edit source]

Explainable AI refers to techniques that provide post-hoc explanations for the results of complex models with low interpretability. For instance, with neural networks or ensemble models, it can be challenging to understand the exact prediction process due to their intricate internal structures. Techniques like SHAP, LIME, and Grad-CAM help explain which factors influenced the predictions.

These tools visualize how much each input variable contributed to the prediction or, in the case of image models, highlight specific areas that influenced the results.