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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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On this page
  • What is GBM?
  • Making the Model
  • Implementation of Interpretability
  • Visualizations

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  1. Implementation of these techniques on different models

GBM - ICE

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Last updated 4 years ago

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What is GBM?

  • Gradient Boosting Machine is a machine learning algorithm that forms an ensemble of weakly predicted decision trees

  • It constructs a forward stage-wise additive model by implementing gradient descent in function space

  • Also known as MART (Multiple Additive Regression Trees) and GBRT (Gradient Boosted Regression Trees)

Making the Model

Dataset: Medical Cost Personal ; Target: charges

The data is trained by calling the GradientBoostingClassifier function from Scikit learn Library

Accuracy:

Implementation of Interpretability

In this section we will interpret a GBM using ICE plots.

We use the Pycebox library and generate ICE plots for "Smoker" feature against out predicted output of "Charges"

Visualizations

ICE plot

In the above plot, we see multiple lines plotted. Each line corresponds to a row in our data. We can see that for some individuals BMI does not affect the charges. But for a few of them, a high BMI seems to increase the charges. Such interpretation can be very useful in showing people the repercussions of having a high BMI.

ICE plot with PDP line

The above plot shows the ICE plot along with the aggregation line shown in black. The aggregation line is the same as the PDP line. The PDP line shows that the overall effect of the age is not much, though the charges increase a small bit after a certain age.

Centered ICE plot

The Centered ICE plot is centering the curves at a certain point in the feature and displaying only the differences in prediction so that it is easy to interpret.

Code Implementation Here