Interpreting Machine Learning Models with LIME
summary
Demonstrates using LIME inside a Jupyter notebook to explain image classifications from a pretrained Inception model by generating 1,000 perturbed variants per image via superpixel masking and fitting a local surrogate model in three lines of code. Shows which superpixels drive the top-5 predictions (e.g., orange at ~92%), gives correct explanations for orange and lemon, and reveals a catastrophic mislabel (a cat classified as a bear), emphasizing that these are local, not global, explanations.
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chapters (11)
- 0:00LIME intro demo
- 3:13Inception model ImageNet
- 6:10Correct prediction orange
- 8:39Correct prediction lemon
- 10:07Misclassified cat bear
- 12:16LIME features limits
- 14:08Q&A
- 15:42LIME alternatives tools
- 16:46LIME impact reliability
- 18:52Model trust Q&A
- 21:16Conclusion
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