Uncertainty Quantification (2): Full Conformal Predictors

[@DataTranslator]

This is masterfully explained. Thank you

[@alexl404]

Thank you for the video. However I didn’t understand the ladder about the conformity scores. You say that it “Shows the ranking of the points in the sorted non-conformity array and not the non-conformity values themselves.” But how do you sort them if not according to their non-conformity value?

[@MLBoost]

You are welcome and glad to see that attention is being paid to every detail.
What I mean by that sentence is that the vertical axis does not show the raw non-conformity scores - it shows the rank of a point in the sorted non-conformity array.
You are correct. We need to first sort that array. For example, imagine we have only two calibration points: the first one with non-conformity = 0.5 and the second with non-conformity 0.7. Then the vertical-axis value associated with the first point will b 2 (because the rank of that point in the sorted non-conformity array is 2) and the one associated with the second point will be 1.

[@abdelhamedmohamed2969]

Thank you for the explanation, this is of high quality

[@MLBoost]

Glad it was helpful!

[@ghifariadamfaza3964]

This video deserves more views and likes!

[@MLBoost]

thank you!

[@valentinussofa4135]

Great lecture. Thank you very much. I subscribed this channel. 🙏

[@MLBoost]

Thanks and welcome!

[@jorgecelis8459]

if the square is a test point why the model need to be fit accounting for it? Thanks for the video

[@jorgecelis8459]

well it was answered in the next video

[@MLBoost]

exactly! Thanks for your comments!

[@cndler23]

amazing!

[@MLBoost]

Thanks!