Decision Tree Pruning

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  • čas přidán 2. 02. 2019
  • Intro to pruning decision trees in machine learning
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Komentáře • 39

  • @lilianaaa98
    @lilianaaa98 Před 2 měsíci

    I‘m pretty fortunate to meet with your channel.

  • @zaidgs
    @zaidgs Před 5 lety +2

    Thank you. Very informative video. I am looking forward for the post-pruning video.

  • @hanzyu5714
    @hanzyu5714 Před 5 lety

    Thank you! Very simple handwriting notes and clear explanation.

  • @juanmacastro2550
    @juanmacastro2550 Před 2 lety

    AWESOME!!! I'm looking forward for the post pruning video!!

  • @awfulprogrammer619
    @awfulprogrammer619 Před 3 lety

    Worth subscribing this channel, Thanks for the wonderful tutorial

  • @MrUsidd
    @MrUsidd Před 4 lety +2

    Amazingly explained!
    Keep up the good work.

  • @ellierchen7597
    @ellierchen7597 Před 4 lety

    It really helps. Thanks a lot. Clear explanation!

  • @abhilashamathur327
    @abhilashamathur327 Před 4 lety

    Very good explanation.... This concept now is very clear for me. Thanks a lot!! :)

  • @yahyazahlane1337
    @yahyazahlane1337 Před 4 lety

    Keep up the great work, one of the best Yt channels out there

  • @Linaiz
    @Linaiz Před 3 lety +1

    Amazing and clear explanation. Thank you!!

  • @haseebali512
    @haseebali512 Před 3 lety +6

    Excellent videos. Can you cover impurity measures, and the intuition behind them?

  • @mikestev8539
    @mikestev8539 Před 4 lety +1

    Good explanation in general, especially that this topic is difficult. But can you suggest where I could learn more about making post-pruning decision trees.

  • @JorgeGomez-kt3oq
    @JorgeGomez-kt3oq Před 6 měsíci

    Love the Channel

  • @arjungoud3450
    @arjungoud3450 Před 2 lety

    Thank you for simple explanation.

  • @perlaramos8783
    @perlaramos8783 Před 4 lety +8

    I'm so glad I found you!!! you remind me of PatrickJMT but for data science!

    • @mINCCC
      @mINCCC Před 3 lety +2

      I'm also glad I found you, dunno who PatrickJMT is, but thank you a lot, cheers!

  • @Fat_Cat_Fly
    @Fat_Cat_Fly Před 3 lety +1

    so good!!

  • @AleeEnt863
    @AleeEnt863 Před rokem

    A big thanks!

  • @punkster36
    @punkster36 Před 3 lety

    Really simple explanation. Would've been more helpful if you spoke a little about the hyperparameters that lead to such pruning.

  • @harshilchaudhary4916
    @harshilchaudhary4916 Před 5 lety +1

    @ritvikmath please provide with a solution

  • @ey654
    @ey654 Před 4 lety +2

    How do you get the 59.4%

  • @harshilchaudhary4916
    @harshilchaudhary4916 Před 5 lety +4

    Please make a post prune video. Thanks!

  • @jomomarkstephen7601
    @jomomarkstephen7601 Před rokem +1

    I just can't get where the 56% and 63% comes in... I am getting lost there

  • @lucusinfabula
    @lucusinfabula Před 3 lety

    I don't even know the fish specifics but it renders the model pretty well. Specifying leaf-note rationale as a graduated-axiom enthuses me.

    • @lucusinfabula
      @lucusinfabula Před 3 lety

      parameterize the 50% shot according to most-critical parameter analysis and so-on down. You can scale any intangible parameter on scaled- preferences (e.g. vmuch yes don't-know no never). The 25%25% shots are review- and revolt-.

  • @cynthiamoricordova5099

    Thank u so much por this video.

  • @zingg7203
    @zingg7203 Před 5 lety +2

    What salmon what tuna?

  • @TheCrisbros15
    @TheCrisbros15 Před 5 lety +1

    I got 59 percent, not 59.2 percent :(

  • @harshilchaudhary4916
    @harshilchaudhary4916 Před 5 lety

    Hi, I think there is a calculation mistake. It is not 59.2 but 56 % please check it.

    • @ey654
      @ey654 Před 4 lety

      I think there is a typing mistake in your comment. It is not 56% but 59% please check it.

  • @juanete69
    @juanete69 Před 2 lety

    Your "7 salmon" looks like a Not salmon :)

  • @harshilchaudhary4916
    @harshilchaudhary4916 Před 5 lety +1

    Please get back AS SOON as Possible.!!!!!!!!!!!!!!!!!!!!!