Apply LIME to Explain, Trust, and Validate Your Predictions for Any ML Model

Explore LIME (Local Interpretable Model-agnostic Explanations) for explaining, trusting, and validating predictions from any machine learning model in this hands-on tutorial. Learn to implement LIME in your ML pipeline through two Jupyter notebooks: one demonstrating LIME explanations with Inception V3 image classification, and another showcasing custom LIME implementation. Discover how to create model explanations for supervised predictions, compare different models, and gain insights into the decision-making process of various algorithms. Dive into topics such as surrogate models, LIME properties, image and tabular data classification explanations, and step-by-step custom explanations using popular models like Inception V3, EfficientNet, and ResNet50.

- Tutorial Introduction
- Why LIME is needed?
- Need for a surrogate model
- LIME Properties
- LIME is not Feature Importance
- Explaining image classification
- Another LIME based explanation
- Tabular data classification explanation
- Two types of explanations
- What is in notebook exercises?
- 1st Original LIME explanation
- Loading Inception V3 model
- LIME library Installation
- Lime Explainer Module
- LIME Explanation Model Creation
- Creating superpixel Image
- Showing Pros and Cons in image
- Showing Pros and Cons with weight higher 0.1 in image
- Analyzing 2nd Prediction
- LIME Custom Implementation
- Loading EffecientNet Model
- Loading LIME class from custom Implementation
- LIME Explanation Results
- Loading ResNet50 Model
- LIME Explanations
- Step by Step Custom Explanations
- Explanations Comparisons
- Saving Notebooks to GitHub
- Recap