This hands on tutorial with 2 Jupyter notebooks explain how you can use LIME (local interpretable model-agnostic explanations) in your machine learning pipeline to explain the results from your model. LIME is model agnostic means can work with almost any model where predictions are supervised in nature and an explanation is required for the prediction by the model.
- The first notebook has detailed step by step implementation of LIME explanations with Inception V3 image classification model.
- The second notebook has a custom implementation of LIME to show how model explanations are created in various stages through custom Python implementations.
GitHub URL for tutorial notebooks:
[ Ссылка ]
▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬
- (00:00) Tutorial Introduction
- (01:07) Why LIME is needed?
- (01:53) Need for a surrogate model
- (02:33) LIME Properties
- (03:45) LIME is not Feature Importance
- (05:14) Explaining image classification
- (06:03) Another LIME based explanation
- (06:41) Tabular data classification explanation
- (07:10) Two types of explanations
- (07:27) What is in notebook exercises?
- (10:01) 1st Original LIME explanation
- (10:40) Loading Inception V3 model
- (13:13) LIME library Installation
- (13:33) Lime Explainer Module
- (14:40) LIME Explanation Model Creation
- (15:54) Creating superpixel Image
- (17:55) Showing Pros and Cons in image
- (18:44) Showing Pros and Cons with weight higher 0.1 in image
- (20:48) Analyzing 2nd Prediction
- (22:48) LIME Custom Implementation
- (25:20) Loading EffecientNet Model
- (26:40) Loading LIME class from custom Implementation
- (27:56) LIME Explanation Results
- (28:40) Loading ResNet50 Model
- (30:39) LIME Explanations
- (33:06) Step by Step Custom Explanations
- (34:56) Explanations Comparisons
- (35:28) Saving Notebooks to GitHub
- (36:00) Recap
Connect
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- Prodramp LLC (@prodramp)
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Content Creator: Avkash Chauhan (@avkashchauhan)
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#mli #xai #ml #lime #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #pytorch #datarobot #datahub #streamlit #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #kaggle #mapbox #lightgbm #xgboost #classification #dataengineering #pandas #keras #tensorflow #tensorboard #cnn #prodramp #avkashchauhan #LIME #modelexplanations #mli #xai
