Machine Learning Model Explainability with LIME in Python

In this video, we learn how to locally explain machine learning models using LIME (Local Interpretable Model-Agnostic Explanations) in Python.
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Timestamps:
(0:00) Intro
(0:17) Decision Tree Example
(6:30) LIME
(17:10) Outro

[@jishnupramod2130]

This is great!! Please do the theoretical aspects of LIME as well...

[@sabbha_mondal]

Its always awsome to learn from you sir. Can you please provide a theoretical explanation too. And off-topic can you help with understanding how to use tqdm module in tkinter.

[@franky12]

Would also like to see a video with SHAP

[@NeuralNine]

Already recorded. Coming soon.

[@user-bp2ol4wi1c]

Great video, could you do LIME for some DL, image classification tasks CNNs maybe or any DL, ideally with pytorch lightning.

[@flashtrack101]

great vid as always! wondering if we gonna get updated tutorial in cython? would love do see that from you. things just makes sense when you explain code.

[@trashchenkov]

Thanks for the video! Will LIME work with neural networks?

[@berkkutukcu7428]

Hey Sir! Thanks for this video! I have a question if could you please answer when you are able to answer? My question, am i able to use this SHAP and LIME for deep learning models. If i am able to use them for deep learning models could you give me some advice to learn it?

[@RamachandraBharadwaj]

this is really useful ! make more ML/ deep learning content !

[@sahil5124]

very helpful, would like more video on Explainable AI and Unsupervised learning

[@insert9124]

How does this compare to shap values? It feels very similar as shap values basically does the same thing. Maybe lime is using shap values under the hood?

[@INDIOBRAVOO]

Hey, bro! Pls make an Ultralytics video.

[@python-developer521]

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