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Explainable-AI
  • Explainable AI
  • Preface
  • How to use this book?
  • Contents
  • What is Explainable AI?
  • Why do we need Explainablity?
  • Explainable systems and Black box systems
  • Types of Explainability Techniques
  • Explainable Models
    • Linear Regression
      • Assumptions
      • Model
      • Statistical Interpretation
    • Decision Trees
      • How Do They Work?
      • Creating the model
      • Interpretation
  • Explainability Techniques for Classical ML
    • SHAP (SHapley Additive exPlanations)
    • Surrogate model
    • LIME (Local Interpretable Model-Agnostic Explanations)
    • PDP (Partial Dependence Plot)
    • ICE (Individual Conditional Expectation Plots)
    • ALE (Accumulated Local Effects Plot)
  • Datasets
    • Medical Cost Personal Dataset
    • Telecom Churn Dataset
    • Sales Opportunity Size Dataset
    • Pima Indians Diabetes Dataset
  • Implementation of these techniques on different models
    • Logistic Regression - SHAP
    • Random Forest - LIME
    • GBM - PDP
    • GBM - ICE
    • Deep Learning - Surrogate
  • Future scope
  • Contributors
  • Citing this Book
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  1. Explainable Models
  2. Linear Regression

Model

PreviousAssumptionsNextStatistical Interpretation

Last updated 3 years ago

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The Google Colab contains the entire code implementation and the best practices to be followed to implement the Linear regression

For this example the data taken is the Insurance Premium data set, the data preparation involved encoding the categorical columns and scaling the data.

After the data preparation, a Linear Regression model is trained on the training data. During the training, the model learns the slope and intercept to approximately fit the training data.

The result of this training yields many different statistical metrics. The ability to understand these statistical metrics allows linear models to be Interpretable. We will go through the statistical metric in more detail in the next section.

Code Implementation here