Influence-based methods
Influence-based methods are used to understand the impact of features present in the dataset on the model's decision-making process. Influence-based methods are widely used and preferred in comparison to other methods as this helps to identify the dominating attributes from the dataset. Identifying the dominating attributes from structured and unstructured data helps us analyze the dominating features' role in influencing the model outcome.
For example, let's say you are working on a classification problem for classifying wolves and Siberian huskies. Let's suppose that after the training and evaluation process, you have achieved a good model with more than 95% accuracy. But when trying to find the important features using influence-based methods for model explainability, you observed that the model picked up the surrounding background as the dominating feature to classify whether it is a wolf or a husky. In such cases, even if your model...