The Quantile Trick

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  • เผยแพร่เมื่อ 30 ต.ค. 2024

ความคิดเห็น • 14

  • @patrickleduc7712
    @patrickleduc7712 หลายเดือนก่อน

    Great videos! Super helpful and I learn something new all the time. Keep up the good work!

  • @Spinnen
    @Spinnen 8 หลายเดือนก่อน +5

    Just wanted to say I love these kind of videos - early days for the channel just wanted to give that positive feedback to continue

    • @probabl_ai
      @probabl_ai  8 หลายเดือนก่อน +1

      (Vincent here) Happy to hear it. Plenty more is on the way!

  • @hernanebraga23
    @hernanebraga23 8 หลายเดือนก่อน +1

    Excellent video, I really like how you explain complex concepts and techniques

  • @andreshoyosidrobo549
    @andreshoyosidrobo549 6 หลายเดือนก่อน +1

    Great video! I think you nailed the explanation. It is nice to see how to use Jupyter widgets as tools to explain ML-related concepts.

    • @probabl_ai
      @probabl_ai  6 หลายเดือนก่อน +1

      Thanks!
      Speaking of widgets. Seen these?
      th-cam.com/video/STPv0jSAQEk/w-d-xo.html
      th-cam.com/video/goaBFxGhp6Y/w-d-xo.html

  • @mikiallen7733
    @mikiallen7733 6 วันที่ผ่านมา

    thanks sir , great exposition , however am wondering as to how one go about adapting such more dynamic approach to a case where the values are time series values are non stationary (main properties change over time , i.e. mean , variance and covariance ) on top of that such type series exhibit heavier tails i.e. probability of having very large values or very small values tend to be higher relative to normal distribution , so your input is highly appreciated
    keep up the good work

  • @GarveRagnara
    @GarveRagnara 8 หลายเดือนก่อน +6

    But what about conformal predictions? :D

    • @probabl_ai
      @probabl_ai  8 หลายเดือนก่อน +5

      (Vincent here) Noted! There's a long todo list for ideas, but I agree conformal predictions deserve attention.

  • @nicolasr8710
    @nicolasr8710 8 หลายเดือนก่อน

    It would be interesting to compare to Bayesian linear regression.

  • @jdt12880
    @jdt12880 8 หลายเดือนก่อน +1

    How does the w parameter map to tge quantile?

  • @alexmolasmartin
    @alexmolasmartin 8 หลายเดือนก่อน +2

    how do you go from the free parameter of the pinball loss to the quantile you want to predict?

    • @probabl_ai
      @probabl_ai  8 หลายเดือนก่อน +1

      (Vincent here) I might recommend playing with the notebook to unravel this one. But I'll keep this idea in mind because it might be an interesting appendix short later.