SP-LIME
In order to make explanation methods more trustworthy, providing an explanation to a single data instance (that is, a local explanation) is not always sufficient, and the end user might want a global understanding of the model to have higher reliability on the robustness of the model. So, the SP-LIME algorithm tries to run the explanations on multiple diverse, yet carefully selected, sets of instances and returns non-redundant explanations.
Now, let me provide an intuitive understanding of the SP-LIME algorithm. The algorithm considers that the time required to go through all the individual local explanations is limited and is a constraint. So, the number of explanations that the end users are willing to examine to explain a model is the budget of the algorithm denoted by B. Let's suppose that X denotes the set of instances; the task of selecting B instances for the end user to analyze for model explainability is defined as the pick step. The pick step is independent...