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- Tutorial Introduction
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Classroom Contents
Apply LIME to Explain, Trust, and Validate Your Predictions for Any ML Model
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- 1 - Tutorial Introduction
- 2 - Why LIME is needed?
- 3 - Need for a surrogate model
- 4 - LIME Properties
- 5 - LIME is not Feature Importance
- 6 - Explaining image classification
- 7 - Another LIME based explanation
- 8 - Tabular data classification explanation
- 9 - Two types of explanations
- 10 - What is in notebook exercises?
- 11 - 1st Original LIME explanation
- 12 - Loading Inception V3 model
- 13 - LIME library Installation
- 14 - Lime Explainer Module
- 15 - LIME Explanation Model Creation
- 16 - Creating superpixel Image
- 17 - Showing Pros and Cons in image
- 18 - Showing Pros and Cons with weight higher 0.1 in image
- 19 - Analyzing 2nd Prediction
- 20 - LIME Custom Implementation
- 21 - Loading EffecientNet Model
- 22 - Loading LIME class from custom Implementation
- 23 - LIME Explanation Results
- 24 - Loading ResNet50 Model
- 25 - LIME Explanations
- 26 - Step by Step Custom Explanations
- 27 - Explanations Comparisons
- 28 - Saving Notebooks to GitHub
- 29 - Recap
