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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

Linear Regression

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Last updated 3 years ago

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A linear model is an attempt to understand the target variable as a linear equation of the features. In other words, a linear model predicts the target(dependent) variable as a weighted sum of the independent variables. Linear regression models are very widely used by statisticians, computer scientists and other people who tackle quantitative problems.

The assumption that all relationships to a target variable are linear and that changing a feature input does not affect the other inputs makes one skeptical to use linear models. That being said, they are one of the oldest and traditional methods of making predictions with high Explainability.

The learned relationships in a linear model can be written for a single row/instance as follows

The equation format makes it very easy to understand how our prediction varies with change in every single feature input. In the coming sections, we will dive deeper into the assumptions of this model, the implementation & interpretation. Links to the Python notebooks are attached towards the end of every section for users.

Credits - Dataquest.io