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Explainable-AI
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Contents

  1. 1.
    ​What is Explainable AI?​
  2. 2.
    ​Why do we need Expalainbility?​
  3. 3.
    ​Explainable Systems and Black Box Systems​
  4. 4.
    ​Types of Explainability Techniques​
  5. 5.
    ​Explainable Models​
    1. 1.
      ​Linear Regression​
      1. 1.
        ​Introduction​
      2. 2.
        ​Assumptions​
      3. 3.
        ​Model​
      4. 4.
        ​Statistical interpretation​
    2. 2.
      ​Decision Trees​
      1. 1.
        ​Introduction​
      2. 2.
        ​How Do They Work?​
      3. 3.
        ​Creating the model​
      4. 4.
        ​Interpretation​
  6. 6.
    ​Model Agonistic Methods​
    1. 1.
      ​SHapley Additive exPlanations​
    2. 2.
      ​Surrogate model​
    3. 3.
      ​Local Interpretable Model-Agnostic Explanations and K-LIME​
    4. 4.
      ​Partial Dependence Plot​
    5. 5.
      ​Individual Conditional Plots​
  7. 7.
    ​Data sets​
    1. 1.
      ​Medical cost personal Dataset​
    2. 2.
      ​Telecom Churn Dataset​
    3. 3.
      ​Sales Opportunity Size Dataset​
    4. 4.
      ​Pima Indians Diabetes Datasets
  8. 8.
    ​Implementation of these techniques on different models​
    1. 1.
      ​Logistic regression​
    2. 2.
      ​Random forest ​
    3. 3.
      ​GBM - PDP​
    4. 4.
      ​GBM - ICE
    5. 5.
      ​Deep Learning​
​
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Last modified 2yr ago