so I hope in next vids we would get review on how practically the `conformal predictors` respect these criteria like 1. `coverage validity` and `efficency` are respected in conformal because the data itself is used in making intervals? 2. how we know its model agnostic? is not involving any model params, enough. also the same thing for distribution free?
also hope to cover this ??? one thing its important is that its probably is not dynamic for point, for i.e. if the loss of a point in the dataset is .098 is in 90% interval, so the other points with the same loss are also in 90% interval, but in more dynamic quantifier, a point with this loss may have 60% interval or 93% interval, I mean `conformal predictor` doesnt take to account the Uncertainty Quantification of input space, so model agnostic and distribution free are not good criteria, instead `model and distro adaptive` are better
How is this different to a quantile approach with X% confidence intervals? I guess the quantile approach would only meet some but not all of the requirements mentioned😅. Interesting stuff.
Using that approach requires one to make an assumption on the underlying distribution where as the conformal method does not. Great question! Thanks for watching!
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Very informative.
Very helpful, thanks a lot!
Very good video. Thanks for making this!
My pleasure!
Excellent 👌 explanation
Thank you 🙂
thanks for the explanation!
You bet!
so I hope in next vids we would get review on how practically the `conformal predictors` respect these criteria
like
1. `coverage validity` and `efficency` are respected in conformal because the data itself is used in making intervals?
2. how we know its model agnostic? is not involving any model params, enough. also the same thing for distribution free?
Thanks for the questions. They will be covered in the next videos of the playlist.
also hope to cover this
??? one thing its important is that its probably is not dynamic for point, for i.e. if the loss of a point in the dataset is .098 is in 90% interval, so the other points with the same loss are also in 90% interval, but in more dynamic quantifier, a point with this loss may have 60% interval or 93% interval, I mean `conformal predictor` doesnt take to account the Uncertainty Quantification of input space, so model agnostic and distribution free are not good criteria, instead `model and distro adaptive` are better
thanks again for the question. But I am not really sure I understand what it is. Could you rephrase?
How is this different to a quantile approach with X% confidence intervals? I guess the quantile approach would only meet some but not all of the requirements mentioned😅. Interesting stuff.
Using that approach requires one to make an assumption on the underlying distribution where as the conformal method does not. Great question! Thanks for watching!
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God I have sinned, of the 70th like. Pls forgive me. 🛐 Amen.
thank you!