Contents
What is model analysis in machine learning?
Abstract. Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems.
What does model interpretability mean?
A (non-mathematical) definition I like by Miller (2017)3 is: Interpretability is the degree to which a human can understand the cause of a decision. The higher the interpretability of a machine learning model, the easier it is for someone to comprehend why certain decisions or predictions have been made.
What is an interpretable model?
Interpretable models are models who explain themselves, for instance from a decision tree you can easily extract decision rules. Model-agnostic methods are methods you can use for any machine learning model, from support vector machines to neural nets.
What should be included in a performance diagnosis?
Performance diagnosis is a problem-defining method. that results in: • accurate identification of actual and desired. organizational, process, and individual performance. levels. • specification of interventions to improve. performance. (p. 38, paragraph).
When does diagnostic refinement become diagnostic verification?
As the list becomes narrowed to one or two possibilities, diagnostic refinement of the working diagnosis becomes diagnostic verification, in which the lead diagnosis is checked for its adequacy in explaining the signs and symptoms, its coherency with the patient’s context (physiology]
Is it necessary to use a model to diagnose?
Without an understanding of the properties of these diagnostics, development and use of new diagnostics, and additional information pertaining to the diagnostics, there is risk that adequate models will be rejected and inadequate models accepted. Thus, a diagnosis of available diagnostics is desirable.
What are the considerations in the diagnostic process?
The chapter describes important considerations in the diagnostic process, such as the roles of diagnostic uncertainty and time.