What is another word for interpretability?
Pronunciation: [ɪntˌɜːpɹɪtəbˈɪlɪti] (IPA) 
Interpretability is a crucial aspect in various fields, including machine learning, data science, and statistics. It refers to the ability of a system or an algorithm to explain its predictions or outputs in a human-understandable manner. However, interpretability can also be described as explainability, comprehensibility, transparency, clarity, and intelligibility. Explainability pertains to the ability to provide insights into the decision-making process of a system, while comprehensibility refers to the ease at which an individual can understand the system's outputs. Transparency and clarity focus on the ability to see through the complexity of a system. Finally, intelligibility entails the capacity to comprehend the system's behavior and outputs. Ultimately, these concepts are all necessary for the effective implementation of any system or algorithm.
