Counterfactual explanations in the context of machine learning and AI are a type of interpretability technique that provides insights into model predictions by generating hypothetical instances, known as counterfactuals. These counterfactuals represent variations of the input data that would lead to a different model prediction while maintaining certain constraints.
I. Key Concepts:
1. Counterfactual Instance:
- A counterfactual instance is an artificial data point that is similar to the original instance but has a different outcome. It is created by perturbing the input features within certain bounds.
2. Objective:
- The goal of counterfactual explanations is to answer questions like “What changes to the input features would have resulted in a different prediction?” This helps users understand the model’s decision-making process.
II. Process of Generating Counterfactual Explanations:
1. Selecting an Instance:
- Choose a specific instance for which you want to generate a counterfactual explanation. This instance is typically…
