Submitted by Visual-Arm-7375 t3_y1zg5r in MachineLearning
I'd like you to give me your opinion and also tell me what could be improved. The video explains in detail the explainable artificial intelligence technique, LIME. The video derives from my bachelor's thesis. Thank you!
Thank you very much for your opinion u/flinsypop! Appreciate it a lot!
I completely agree with what you say, and I'll take that in mind for the next videos.
Awesome. Cant wait!
You might want to include a slide on critical takes. For example, see this popular article: https://towardsdatascience.com/whats-wrong-with-lime-86b335f34612
Nice! Thank you :)
I have used LIME and I thought this explanation was a great start. I agree with flinsypop that an in-depth example would be good. Josh Starmer on Statquest does a nice job of this, for example when he explains xgboost.
Also, I think if you're interested in continuing to make educational YouTube videos, you should work on your accent. It is quite hard for a native speaker to understand and I expect non-natives would struggle to access your great content. I offer this only to help you so sorry if you take it poorly.
Keep up the great work!
Thank you very much for the comment! I don't take it badly at all. Constructive criticism is always welcome. Regarding my accent, I really try to give the best of myself but it's innate haha, I'm sorry. I will try to improve it although I don't know how. Also, I recorded with the laptop, I have no micro :( However, I provided detailed subtitles in the video!!
LIME has a lot of problems and I think it is worth mentioning more of them. As an example, this paper shows that the top features in a LIME explanation of an outcome are often neither necessary nor sufficient to cause that outcome.
Well yeah, LIME tells you about an existing model, right? So if multiple features are correlated then a model may drop one of the features and the explanations will say that the drop model has no predictive power while the correlated feature is important. But we can drop the important feature and train an equally good model (maybe even better).
I think the paper is saying that LIME may explain a model's prediction using features that are actually of little consequence to the model. I have a feeling that this is tied to the instability problem: do 2 runs of LIME to explain the same point and get 2 significantly different explanations.
But is this LIME's problem? I mean, it is the model that is not taking into account the correlated feature, not LIME. LIME just looks at the original model.
Damn! The paper is really interesting u/TenaciousDwight! Thanks :) Appreciate it.
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flinsypop t1_is0cbrw wrote
This is a nice brief introduction. Where you could improve is showing how each part of the presentation is mapped to code, so people can play around with it. My advice would be to link to the lime tutorials and fill in any gaps with notebooks of your own. If you can direct your viewers to be practice what you explain and also have safety nets where you explain common problems and solutions, you can differentiate your content from the dozens of other content creators explaining the same tools and concepts you are.
I do have bias here though, I dislike slides and slides of mathematical notation but you did a good job of breaking it up with visuals in the middle. However, in the second half, it would have been better if you referred back to examples from the first half as you go along. Using different examples can be fine but, in my experience explaining it to colleagues, the lack of continuity can stun lock people. For example people might wonder what exactly the perturbated dataset could look like for the images at the start. You could show the output of lime for the husky picture compared to the same picture with added noise that would have been generated in the "perturbed" dataset.