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An Evaluation System
  • An Evaluation System for Interpretable Machine Learning
  • 1. Abstract
  • 2. Introduction
  • 3. Interpretable Machine Learning
    • 3.1 Definition
    • 3.2 Methods
      • 3.2.1 Machine Learning Model
      • 3.2.2 Model-Agnostic
      • 3.2.3 Sample Theory
  • 4. Evaluation System of Interpretable Machine Learning
    • 4.1 Definition
    • 4.2 The Structure of the Evaluation System
      • 4.2.1 Interpretable Models(Model specific)
      • 4.2.2 Model Agnostic
      • 4.2.3 Human Explanations
    • 4.3 Reference Score Table
  • 5. Customized - Interpretable Machine Learning Model
    • 5.1 Why & How the user customize their model?
    • 5.2 Example
      • 5.2.1 Suggestion Lists
      • 5.2.2 Notebook
    • 5.3 Results & Explanation
  • 6. Discussion
  • 7. Citation and License
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  1. 4. Evaluation System of Interpretable Machine Learning

4.2 The Structure of the Evaluation System

Structure of the Evaluation System

Previous4.1 DefinitionNext4.2.1 Interpretable Models(Model specific)

Last updated 4 years ago

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The evaluation of interpretable machine learning can be distinguished by restricting the complexity of the machine learning model or by applying methods that analyze the model after training. Therefore, most of the evaluation system evaluates the interpretable machine learning model by two criteria. One is the evaluation of the interpretability of intrinsically interpretable models, the other is the interpretability of model-agnostic. But we think the human explanations are equally important as the first two criteria, which is how to use language or diagram to present or explain the model-specific and model-agnostic methods. Under each criterion, we have lists of keywords on different perspectives which we will introduce to you in this chapter.

Sructure of the Evaluation System