Machine Learning Model Explainability with LIME in Python
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- čas přidán 1. 06. 2024
- 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 - Věda a technologie
This is great!! Please do the theoretical aspects of LIME as well...
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.
Would also like to see a video with SHAP
Already recorded. Coming soon.
Great video, could you do LIME for some DL, image classification tasks CNNs maybe or any DL, ideally with pytorch lightning.
this is really useful ! make more ML/ deep learning content !
Thanks for the video! Will LIME work with neural networks?
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.
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?
very helpful, would like more video on Explainable AI and Unsupervised learning
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?
Hey, bro! Pls make an Ultralytics video.
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