In this chapter, we start our journey through XAI model agnostic methods that are, as we said, potent techniques to produce explanations without relying on ML models internals that are “opaque.” Additionally, we will explore Accumulated Local Effects (ALE), a method that addresses limitations of Partial Dependence Plots when dealing with correlated features—a common scenario in real-world datasets. ALE provides more accurate interpretations by focusing on local changes in the feature space rather than averaging across potentially unrealistic feature combinations.