2.1. Global

Global interpretability refers to the understanding of model behavior overall, providing insight into how different features affect predictions across the entire dataset.

Global interpretability is essential for understanding the overall patterns and relationships in the model. This means that instead of focusing on a single prediction, global interpretability looks at how all features contribute to the model's predictions generally. For instance, if a model predicts home prices, global interpretability would help identify how factors like location, size, and age of the property influence the prices across various instances.

Imagine a chef who has created a signature dish. Instead of just tasting a single plate, a food critic analyzes the entire menu to understand how different ingredients contribute to the flavors of the dish. Global interpretability is similar; it allows us to see the whole model and how each feature, like ingredients, plays a role in the outcome.

Local interpretability deals with providing an explanation for a specific prediction, answering questions like, 'Why did the model predict X for Y?'

Local interpretability focuses on individual predictions made by the model, explaining why a specific outcome occurred for a particular instance. For example, if the model predicts that a specific loan application will be denied, local interpretability helps us understand the precise features and their values that led to that prediction, such as the applicant's credit score or income level.

Think of a doctor diagnosing a patient. While the doctor may know general signs of a disease, they need to assess the specific symptoms of this patient to make a diagnosis. Similarly, local interpretability provides insights into the reasoning behind each individual prediction.

The types of interpretability can be intrinsic, where the model is naturally interpretable (like decision trees), or post-hoc, where explanations are provided after model training (like LIME and SHAP).

Intrinsic interpretability refers to models that are designed to be interpretable by their nature, such as linear regression, where the output relationships are clear from the coefficients. Post-hoc interpretability, on the other hand, applies to more complex models (like deep learning models) where external methods are used to extract explanations after the model has been trained. Tools like LIME and SHAP are examples of post-hoc methods that help illustrate how different features influence specific predictions.

Think of intrinsic interpretability as reading a straightforward book with clear language. You can easily understand the plot without any additional help. Post-hoc interpretability is like analyzing a complicated novel; you might need a study guide to comprehend the themes and character motivations after reading.

--