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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. 3. Interpretable Machine Learning

3.2 Methods

We have methods listed below to proceed. Below are the methods, that diverge under the Interpretable Machine Learning

  • Machine Learning Models

  • Model-Agnostic

  • Sample Theory

Machine Learning Models - will be helping us with a specific algorithm to build the interpretable models and start working with it, in addition to it to help us explain the working

Model-Agnostic - Separating the explanations from the machine learning model (= model-agnostic interpretation methods) has some advantages, and we are going to discuss it in the coming sections.

Sample Theory - Is not related to the model interpretability, but a research sub-topic that would help us increase the computational speed to achieve our goal for interpretation and dealing with huge data to build the interpretable models. This is just a supporting method for our book, and should not be considered important for an Evaluation System.

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Last updated 4 years ago

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