Working with model interpretability
Model interpretability in ML refers to the ability to understand and explain how a particular model makes predictions or decisions. Interpretable models provide clear insights into the features or variables that are most influential in the model’s decision-making process. This is particularly important in domains where the decision-making process needs to be transparent and understandable, such as healthcare, finance, and legal systems.
Although you can never explain 100% why a model makes a prediction, you can use explainers to understand which features affect the results. Explainers can help us provide global explanations; for example, which features affect the overall behavior of the model or local explanations that provide us with information on what influenced an individual prediction.
Let us explore some methods we can use to achieve model interpretability:
