Machine Learning Explainability

Brought by: Kaggle

Overview

Extract human-understandable insights from any model.

  • Why and when do you need insights?
  • What features does your model think are important?
  • How does each feature affect your predictions?
  • Understand individual predictions
  • Aggregate SHAP values for even more detailed model insights

Syllabus

  • Use Cases for Model Insights
  • Permutation Importance
  • Partial Plots
  • SHAP Values
  • Advanced Uses of SHAP Values

Taught by

Dan Becker

Machine Learning Explainability
Go to course

Machine Learning Explainability

Brought by: Kaggle

  • Kaggle
  • Free
  • English
  • Certificate Available
  • Available at any time
  • All
  • N/A