Gradient Boost Part 3 (of 4): Classification

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  • Опубликовано: 11 янв 2025

Комментарии • 551

  • @statquest
    @statquest  4 года назад +28

    NOTE: Gradient Boost traditionally uses Regression Trees. If you don't already know about Regression Trees, check out the 'Quest: ruclips.net/video/g9c66TUylZ4/видео.html Also NOTE: In Statistics, Machine Learning and almost all programming languages, the default base for the log function, log(), is log base 'e' and that is what I use here.
    Support StatQuest by buying my books The StatQuest Illustrated Guide to Machine Learning, The StatQuest Illustrated Guide to Neural Networks and AI, or a Study Guide or Merch!!! statquest.org/statquest-store/

    • @parijatkumar6866
      @parijatkumar6866 4 года назад +1

      I am a bit confused. The first Log that you took : Log(4/2) - was that to some base other than e? Cause e^(log(x)) = x for log to the base e
      And hence the probability will be simply 2/(1+2) = 2/3 = No of Yes / Total Obs = 4/6 = 2/3
      Pls let me know if this is correct.

    • @statquest
      @statquest  4 года назад +2

      @@parijatkumar6866 The log is to the base 'e', and yes, e^(log(x)) = x. However, sometimes we don't have x, we just have the log(x), as is illustrated at 9:45. So, rather than use one formula at one point in the video, and another in another part of the video, I believe I can do a better job explaining the concepts if I am consistent.

    • @jonelleyu1895
      @jonelleyu1895 Год назад

      For Gradient Boost for CLASSIFICATION, because we convert the categorical targets(No or Yes) to probabilities(0-1) and the residuals are calculated from the probabilities, when we build a tree, we still use REGRESSION tree, which use sum of squared residuals to split the tree. Is it correct? Thank you.

    • @statquest
      @statquest  Год назад +1

      @@jonelleyu1895 Yes, even for classification, the target variable is continuous (probabilities instead of Yes/No), and thus, we use regression trees.

  • @weiyang2116
    @weiyang2116 4 года назад +175

    I cannot imagine the amount of time and effort used to create these videos. Thanks!

    • @statquest
      @statquest  4 года назад +31

      Thank you! Yes, I spent a long time working on these videos.

  • @primozpogacar4521
    @primozpogacar4521 4 года назад +26

    Love these videos! You deserve a Nobel prize for simplifying machine learning explanations!

  • @rodrigovm
    @rodrigovm 6 дней назад +1

    Thank's Josh. You have no idea how much you've helped me throughout your videos. God bless you.

  • @debsicus
    @debsicus 3 года назад +52

    This content shouldn’t be free Josh. So amazing Thank You 👏🏽

    • @statquest
      @statquest  3 года назад +3

      Thank you very much! :)

  • @sameepshah3835
    @sameepshah3835 7 месяцев назад +3

    Thank you so much Josh, I watch 2-3 videos everyday of your machine learning playlist and it just makes my day. Also the fact that you reply to most of the people in the comments section is amazing. Hats off. I only wish the best for you genuinely.

    • @statquest
      @statquest  7 месяцев назад +1

      bam!

    • @sameepshah3835
      @sameepshah3835 7 месяцев назад +1

      @@statquest Double Bam!
      Bam?

    • @enicay7562
      @enicay7562 3 месяца назад

      @@sameepshah3835 Triple Bam!

  • @jagunaiesec
    @jagunaiesec 5 лет назад +35

    The best explanation I've seen so far. BAM! Catchy style as well ;)

    • @statquest
      @statquest  5 лет назад

      Thank you! :)

    • @arunavsaikia2678
      @arunavsaikia2678 5 лет назад +1

      @@statquest are the individual trees which are trying to predict the residuals regression trees?

    • @statquest
      @statquest  5 лет назад

      @@arunavsaikia2678 Yes, they are regression trees.

  • @OgreKev
    @OgreKev 5 лет назад +55

    I'm enjoying the thorough and simplified explanations as well as the embellishments, but I've had to set the speed to 125% or 150% so my ADD brain can follow along.
    Same enjoyment, but higher bpm (bams per minute)

  • @dhruvjain4774
    @dhruvjain4774 4 года назад +10

    you really explain complicated things in very easy and catchy way.
    i like the way you BAM

  • @xinjietang953
    @xinjietang953 Год назад +2

    Thanks for all you've done. You know your videos is first-class and precision-promised learning source for me.

  • @asdf-dh8ft
    @asdf-dh8ft 4 года назад +2

    Thank you very much! Your step by step explanation is very helpful. It gives to people with poor abstract thinking like me chance to understand all math of these algorithms.

    • @statquest
      @statquest  4 года назад +1

      Glad it was helpful!

  • @igormishurov1876
    @igormishurov1876 5 лет назад +7

    Will recommend the channel for everyone study the machine learning :) Thanks a lot, Josh!

  • @rameshh3821
    @rameshh3821 5 месяцев назад +2

    You have explained the Gradient Boosting Regressor and Classifier very well. Thank you!

  • @narasimhakamath7429
    @narasimhakamath7429 4 года назад +4

    I wish I had a teacher like Josh! Josh, you are the best! BAAAM!

  • @cmfrtblynmb02
    @cmfrtblynmb02 3 года назад +1

    Finally a video that shows the process of gradent boosting. Thanks a lot.

  • @Valis67
    @Valis67 3 года назад +2

    That's an excellent lesson and a unique sense of humor. Thank you a lot for the effort in producing these videos!

  • @lemauhieu3037
    @lemauhieu3037 2 года назад +2

    I'm new to ML and these contents are gold. Thank you so much for the effort!

  • @dankmemer9563
    @dankmemer9563 4 года назад +2

    Thanks for the video! I’ve been going on a statquest marathon for my job and your videos have been really helpful. Also “they’re eating her...and then they’re going eat me!....OH MY GODDDDDDDDDDDDDDD!!!!!!”

  • @mayankamble2588
    @mayankamble2588 9 месяцев назад +1

    This is amazing. This is the nth time I have come back to this video!

  • @rishabhkumar-qs3jb
    @rishabhkumar-qs3jb 3 года назад +1

    Fantastic video , I was confused about the gradient boosting, after watching all parts of gb technique from this channel, I understood it very well :)

  • @juliocardenas4485
    @juliocardenas4485 2 года назад +1

    Yet again. Thank you for making concepts understandable and applicable

  • @soujanyapm9595
    @soujanyapm9595 3 года назад +1

    Amazing illustration of a complicated concept. This is best explanation. Thank you so much for all your efforts in making us understand the concepts very well !!! Mega BAM !!

  • @aweqweqe1
    @aweqweqe1 3 года назад +1

    Respect and many thanks from Russia, Moscow

  • @amitv.bansal178
    @amitv.bansal178 2 года назад +1

    Absolutely wonderful. You are are my guru and a true salute to you

  • @CodingoKim
    @CodingoKim Год назад +8

    my life has been changed for 3 times. First, when I met Jesus. Second, when I found out my true live. Third, it's you Josh

  • @tymothylim6550
    @tymothylim6550 3 года назад +2

    Thank you Josh for another exciting video! It was very helpful, especially with the step-by-step explanations!

    • @statquest
      @statquest  3 года назад +1

      Hooray! I'm glad you appreciate my technique.

  • @umeshjoshi5059
    @umeshjoshi5059 4 года назад +3

    Love these videos. Starting to understand the concepts. Thank you Josh.

  • @user-gr1qk3gu4j
    @user-gr1qk3gu4j 5 лет назад +1

    Very simple and practical lesson. I did created a worked sample based on this with no problems.
    It might be obvious, but not explained there, that initial mean odd should be more than 1. It might be explained as odd of more rare event should be closer to zero.
    Glad to see this video arrived just at the time I started to interest this topic.
    I guess it will become a "bestseller"

  • @rrrprogram8667
    @rrrprogram8667 5 лет назад +2

    I have beeeeennnn waiting for this video..... Awesome job Joshh

  • @FelipeArayaable
    @FelipeArayaable 9 месяцев назад +1

    Hello Josh! I think that there might be a mistake in methodology at min 5:11 compared to what you showed in part 4 of the series for computing the residual. In this video, the first set of residuals you computed it as (Observed - log(odds) = residuals) and in part 4 you calculate it as (Observed - probability = residuals), so in this scenario where we have Observed as 1, log(odds) as 0.7, and p as 0.66, shouldn't the residuals be (1 - 0.66 = 0.33) instead of (1 - 0.7 - 0.3)?
    Love your videos and I am a huge fan!

    • @statquest
      @statquest  9 месяцев назад

      I think you are confused, perhaps because the log(odds) = log(4/2) = 0.7 = 4/6 = probability. So, in this specific situation, both the log(odds) and the probability are the same. Thus, when we calculate the residuals, we use the probability. The equation is Residual = (observed - probability), as can been see in earlier at 4:49

  • @Just-Tom
    @Just-Tom 4 года назад +3

    I was wrong! All your songs are great!!!
    Quadruple BAM!

  • @ElderScrolls7
    @ElderScrolls7 4 года назад +1

    Another great lecture by Josh Starmer.

    • @statquest
      @statquest  4 года назад +1

      Hooray! :)

    • @ElderScrolls7
      @ElderScrolls7 4 года назад +1

      @@statquest I actually have a draft paper (not submitted yet) and included you in the acknowledgements if that is ok with you. I will be very happy to send it to you when we have a version out.

    • @statquest
      @statquest  4 года назад +1

      @@ElderScrolls7 Wow! that's awesome! Yes, please send it to me. You can do that by contacting me first through my website: statquest.org/contact/

    • @ElderScrolls7
      @ElderScrolls7 4 года назад +1

      @@statquest I will!

  • @SergioPolimante
    @SergioPolimante 3 года назад +1

    man, you videos are just super good, really.

  • @FabricioVladimirVincesVinces
    @FabricioVladimirVincesVinces 6 месяцев назад +1

    It is perfectly understood. Thank you so much!

    • @statquest
      @statquest  6 месяцев назад

      Glad it was helpful!

  • @Mars7822
    @Mars7822 2 года назад +1

    Super Cool to understand and study, Keep Up master..........

  • @marjaw6913
    @marjaw6913 3 года назад +1

    Thank you so much for this series, I understand everything thanks to you!

  • @dvdmrn
    @dvdmrn 4 года назад +1

    Why when plugging into the logistic function around 2:42 is 1+e^log(4/2) in the denominator and not 1+ e^-log(4/2)? (Given the sigmoid is 1/[1+e^-x]). When I try plugging in e^(log(4/2))/[1+e^log(4/2)] I get 0.574, and when I use e^(log(4/2))/[1+e^-log(4/2)] I get something closer (0.776). What base is the log in? (I tried base 2 and base e but got diff results still)

    • @statquest
      @statquest  4 года назад

      In statistics, machine learning and most programming languages, the default log function is log to the base 'e'. So, in all of my videos, I use log to the base 'e'. In this video, we use e^log(odds) / (1 + e^log(odds)) to convert the odds. This equation is derived here: ruclips.net/video/BfKanl1aSG0/видео.html
      As to why you're not getting 0.7 when you do the math, you need to double check that you are using base 'e'. For example, when I use base 10, I get the same result you got:
      > exp(log10(4/2)) / (1 + exp(log10(4/2)))
      [1] 0.5746943
      However, when I use base 'e', I get the result in the video:
      > exp(log(4/2)) / (1 + exp(log(4/2)))
      [1] 0.6666667

  • @patrickyoung5257
    @patrickyoung5257 4 года назад +5

    You save me from the abstractness of machine learning.

  • @gonzaloferreirovolpi1237
    @gonzaloferreirovolpi1237 5 лет назад +1

    Already waiting for Part 4...thanks as always Josh!

    • @statquest
      @statquest  5 лет назад +1

      I'm super excited about Part 4 and should be out in a week and a half. This week got a little busy with work, but I'm doing the best that I can.

  • @SoukainaGhafel
    @SoukainaGhafel 10 месяцев назад +1

    thanks alot , ur videos helped me too much, plz keep going

  • @siyizheng8560
    @siyizheng8560 5 лет назад +2

    All your videos are super amazing!!!!

  • @abhilashsharma1992
    @abhilashsharma1992 4 года назад +5

    Best original song ever in the start!

    • @statquest
      @statquest  4 года назад +2

      Yes! This is a good one. :)

  • @ayahmamdouh8445
    @ayahmamdouh8445 3 года назад +1

    Hi Josh, great video.
    Thank you so much for your great effort.

  • @vans4lyf2013
    @vans4lyf2013 3 года назад +7

    I wish I could give you the money that I pay in tuition to my university. It's ridiculous that people who are paid so much can't make the topic clear and comprehensible like you do. Maybe you should do teaching lessons for these people. Also you should have millions of subscribers!

    • @statquest
      @statquest  3 года назад

      Thank you very much!

  • @vinayakgaikar154
    @vinayakgaikar154 2 года назад +1

    nice explanation and easy to understand thanks bro

  • @HamidNourashraf
    @HamidNourashraf Год назад +1

    the best video for GBT

  • @sidagarwal43
    @sidagarwal43 4 года назад +1

    Amazing and Simple as always. Thank You

    • @statquest
      @statquest  4 года назад

      Thank you very much! :)

  • @ugwuchiagozie6990
    @ugwuchiagozie6990 4 месяца назад +1

    God bless you josh
    I really appreciate

  • @ΒΑΣΙΛΗΣ_ΛΕΒΕΝΤΑΡΟΣ
    @ΒΑΣΙΛΗΣ_ΛΕΒΕΝΤΑΡΟΣ 3 месяца назад +1

    First of all, I would like to thank you, Dr. Josh, for all these great videos. I would like to ask how important, in your experience, it is to understand the algorithms mathematics, as you analyze them in parts 2 and 4, especially for people who want to work in the analysis of biological data. Thanks a lot again! you really helped me understand many machine learning topics.

    • @statquest
      @statquest  3 месяца назад +1

      One of the reasons I split these videos into "main ideas" and "mathematical details" was I felt that the "main ideas" were more important for most people. The details are interesting, and helpful if you want to build your own tree based method, but not required.

    • @ΒΑΣΙΛΗΣ_ΛΕΒΕΝΤΑΡΟΣ
      @ΒΑΣΙΛΗΣ_ΛΕΒΕΝΤΑΡΟΣ 3 месяца назад +1

      @statquest Thank you for your reply! Also, I would like to thank you again for all this knowledge that you provide. I have never seen a better teaching methodology than yours ! :)

    • @statquest
      @statquest  3 месяца назад

      @@ΒΑΣΙΛΗΣ_ΛΕΒΕΝΤΑΡΟΣ Thank you very much! :)

  • @koderr100
    @koderr100 2 года назад +1

    thanks for videos. best of anything else I did see. Will use this 'pe-pe-po-pi-po" as message alarm on phone)

  • @bhavanisankarlenka
    @bhavanisankarlenka 2 месяца назад +1

    I'm Thankful to u Joshhh , HURRAY ! GIGA BAMM!

  • @yulinliu850
    @yulinliu850 5 лет назад +1

    Excellent as always! Thanks Josh!

  • @rrrprogram8667
    @rrrprogram8667 5 лет назад +2

    So finallyyyy the MEGAAAA BAMMMMM is included.... Awesomeee

    • @statquest
      @statquest  5 лет назад +2

      Yes! I was hoping you would spot that! I did it just for you. :)

    • @rrrprogram8667
      @rrrprogram8667 5 лет назад +1

      @@statquest i was in office when i first wrote the comment earlier so couldn't see the full video...

  • @mengdayu6203
    @mengdayu6203 5 лет назад +17

    How does the multi-classification algorithm work in this case? Using one vs rest method?

    • @questforprogramming
      @questforprogramming 4 года назад +2

      It's been over 11 months and no reply from josh... bummer

    • @Nushery12
      @Nushery12 4 года назад +2

      have the same question

    • @ketanshetye5029
      @ketanshetye5029 4 года назад +1

      @@Nushery12 well, we could use one vs rest approach

    • @Andynath100
      @Andynath100 4 года назад +3

      It uses a Softmax objective in the case of multi-class classification. Much like Logistic(Softmax) regression.

  • @rungrawin1994
    @rungrawin1994 3 года назад +2

    Listening to your song makes me thinking of Phoebe Buffay haha.
    Love it, anyway !

    • @statquest
      @statquest  3 года назад +1

      See: ruclips.net/video/D0efHEJsfHo/видео.html

    • @rungrawin1994
      @rungrawin1994 3 года назад +1

      ​@@statquest Smelly stat, smelly stat, It's not your fault (to be so hard to understand)

    • @rungrawin1994
      @rungrawin1994 3 года назад

      @@statquest btw i like your explanation on gradient boost too

  • @parthsarthijoshi6301
    @parthsarthijoshi6301 3 года назад +1

    THIS IS A BAMTABULOUS VIDEO !!!!!!

  • @cmfrtblynmb02
    @cmfrtblynmb02 3 года назад +2

    How do you create the classification trees using residual probabilities? Do you stop using some kind of purity index during the optimization in that case? Or do you use regression methods?

    • @statquest
      @statquest  3 года назад

      We use regression trees, which are explained here: ruclips.net/video/g9c66TUylZ4/видео.html

  • @이형석-g9m
    @이형석-g9m 2 года назад +1

    Great video! Thank you!

  • @123chith
    @123chith 5 лет назад +16

    Thank you so much can you please make a video for Support Vector Machines

  • @suryan5934
    @suryan5934 4 года назад +4

    Now I want to watch Troll 2

    • @statquest
      @statquest  4 года назад +1

      :)

    • @AdityaSingh-lf7oe
      @AdityaSingh-lf7oe 4 года назад +2

      Somewhere around the 15 min mark I made up my mind to search this movie on google

    • @suryan5934
      @suryan5934 4 года назад

      @@AdityaSingh-lf7oe bam

  • @abissiniaedu6011
    @abissiniaedu6011 Год назад +1

    Your are very helpful, thank you!

  • @anshvashisht8519
    @anshvashisht8519 Год назад +1

    really liked this intro

  • @haitaowu5888
    @haitaowu5888 3 года назад

    Hi, I have a few questions: 1. How do we know when GBDT algorithms stops( except the M, number of trees) 2. how do I choose value for the M, how do I know this is optimal ?
    Nice work by the way, best explanation I found on the internet.

    • @statquest
      @statquest  3 года назад

      You can stop when the predictions stop improving very much. You can try different values for M and plot predictions after each tree and see when predictions stop improving.

    • @haitaowu5888
      @haitaowu5888 3 года назад +1

      @@statquest thank you!

  • @SomeGuy-q1d
    @SomeGuy-q1d 5 лет назад +3

    Gradient Boost: BAM
    Gradient Boost: Double BAM
    Gradient Boost: Triple BAM
    Gradient Boost: Quadruple BAM
    Great Gradient Boost franchise)

    • @statquest
      @statquest  5 лет назад

      Thanks so much! XGBoost is next! It's an even bigger and more complicated algorithm, so it will be many, many BAMs! :)

    • @kunlunl6723
      @kunlunl6723 4 года назад

      I thought you are ganna say PentaBAM -> Unstoppable -> Godlike (if you play League of Legend

  • @61_shivangbhardwaj46
    @61_shivangbhardwaj46 3 года назад +1

    You r amazing sir! 😊 Great content

  • @siddharthvm8262
    @siddharthvm8262 3 года назад +1

    Bloody awesome 🔥

  • @SoubhikBhattacharya
    @SoubhikBhattacharya Год назад

    @statquest you mentioned at 10:45 that we build a lot of trees. Are you trying to refer to bagging or having different tree at each iteration?

    • @statquest
      @statquest  Год назад

      Each time we build a new tree.

  • @bryanparis7779
    @bryanparis7779 2 года назад

    6:42 The transformation formula has as numerator "something came from log(odds)" , while denominator has probabilities.
    The output for this fraction is something in terms of log(odds). I don't get the point of why ... Maybe because we have lets say: log(prob)/prob=log( )???

    • @statquest
      @statquest  2 года назад +1

      Agreed - I'm not really sure how this works, but it does.

  • @hamzael2200
    @hamzael2200 4 года назад

    HEY ! THANKS FOR THIS AWESOME VIDEO. I HAVE A QUESTION : IN THE 12:00 MIN HOW DID YOU BUILD THIS NEW TREE? WHAT WAS THE CRITERIA FOR CHOOSING AGE LESS THAN 66 AS THE ROOT ?

    • @statquest
      @statquest  4 года назад

      Gradient Boost uses Regression Trees: ruclips.net/video/g9c66TUylZ4/видео.html

  • @siddharth4251
    @siddharth4251 Год назад +1

    subscribed sir....nice efforts sir

  • @ankailiu3365
    @ankailiu3365 Год назад

    Just curious at 3:00 why we dont just count the probability instead we use log odd and obtain probability from it. This two probabilities are essentially the same thing arn't them? I guess they are the same and the reason being we want to perform gradient boost tree on log odd instead of directly on probability since it might cause over shot and obtain something not in [0,1].

    • @statquest
      @statquest  Год назад

      That's correct. By using the log(odds), which go from negative infinity to positive infinity, we can add as many trees together as we want without any fear of going out of range. In contrast, if we used probabilities, we would have to check to make sure we stayed within values between 0 and 1.

  • @madaramarasinghe829
    @madaramarasinghe829 2 месяца назад

    Thanks again for a wonderful video. At 6:00 and 12:00, regression trees are built to fit residuals. How are the conditions obtained to building these trees?

    • @statquest
      @statquest  2 месяца назад

      I'm not sure I understand your question. Can you clarify? We just build trees with the residuals as the target variable.

    • @madaramarasinghe829
      @madaramarasinghe829 2 месяца назад

      @@statquest At 12:00 it checks "Age < 66". Why do we specifically check for "Age < 66" instead of say "Age < 71"? Was the value 66 obtained based on some mathematical basis? Thanks again for promptly responding to all the questions.

    • @statquest
      @statquest  2 месяца назад

      @@madaramarasinghe829 Gradient Boost uses regression trees. To learn how regression trees are built, see: ruclips.net/video/g9c66TUylZ4/видео.html

  • @nayrnauy249
    @nayrnauy249 3 года назад +1

    Josh my hero!!!

  • @sid9426
    @sid9426 4 года назад +1

    Hey Josh,
    I really enjoy your teaching. Please make some videos on XG Boost as well.

    • @statquest
      @statquest  4 года назад

      XGBoost Part 1, Regression: ruclips.net/video/OtD8wVaFm6E/видео.html
      Part 2 Classification: ruclips.net/video/8b1JEDvenQU/видео.html
      Part 3 Details: ruclips.net/video/ZVFeW798-2I/видео.html
      Part 4, Crazy Cool Optimizations: ruclips.net/video/oRrKeUCEbq8/видео.html

  • @jt007rai
    @jt007rai 2 года назад

    thanks for this video! qq - @7:45 : How did the output of the tree have negative values in their leaf? Even we use it as a classifier, shouldn't the value be in terms of ratio of positives to negatives?

    • @statquest
      @statquest  2 года назад

      The output from each tree (the values in the leaves) are on the log(odds) scale, which we later convert into a probability of being one of the two classifications. For details, see: 14:27

  • @hitesh8383
    @hitesh8383 7 месяцев назад

    Thanks for this video. But one question. Does the tree that you constructed for predicting residuals at 5:30 use sum of squared errors as in case of regression trees or GINI index as in case of decision trees? Since we have only two target values

    • @statquest
      @statquest  7 месяцев назад

      In a pinned comment I wrote "Gradient Boost traditionally uses Regression Trees. If you don't already know about Regression Trees, check out the 'Quest: ruclips.net/video/g9c66TUylZ4/видео.html"

  • @7_Maverick_7
    @7_Maverick_7 3 года назад

    Hey josh great videos!! But I want to ask a doubt around 6:40. To add the leaf and tree's prediction, we are converting tree's prediction through that formula to convert it into log(odds) format, the same type as of leaf and continue to do the same process for each subsequent trees, Right.
    My question is why not we convert the initial single leaf's output to probability format for once and spare all the predictions of further trees from that conversion formula ?

    • @statquest
      @statquest  3 года назад +1

      Because the log(odds) goes from negative infinity to positive infinity, allowing us to add as many trees as we please without having to worry that we will go too far. In contrast, if we used probability, then we would have hard limits at 0 and 1, and then we would have to worry about adding too many trees and going over or under etc.

  • @rodrigomaldonado5280
    @rodrigomaldonado5280 5 лет назад +4

    Hi Statquest would you please make a video about naive bayes? Please it would be really helpful

  • @tumul1474
    @tumul1474 5 лет назад +2

    amazing as always !!

  • @cerealnashta
    @cerealnashta Год назад +1

    The legendary MEGA BAM!!

  • @rvstats_ES
    @rvstats_ES 4 года назад +1

    Congrats!! Nice video! Ultra bam!!

    • @statquest
      @statquest  4 года назад

      Thank you very much! :)

  • @vijaykumarlokhande1607
    @vijaykumarlokhande1607 3 года назад

    I salute your hardwork, and mine too

  • @VijayBhaskarSingh
    @VijayBhaskarSingh 5 лет назад

    I think there is a mistake, in the way the tree classified to predict after 14:41. As Age = 25 and the explanation takes to right, which shouldn't have been. Typically, "Yes" follows to the direction of the Arrow and a "No" to the left. However, its contrary to the assumptions. Correct me if I am wrong.
    Great Explanation.

    • @statquest
      @statquest  5 лет назад

      There are different conventions for drawing trees. The one I follow is that if the statement is "true", you take the left branch. If the statement is "false", you take the right branch. I try to be consistent with this.

  • @しゅんぷ-x9r
    @しゅんぷ-x9r 5 лет назад +1

    Thank you for good videos!

  • @abdelhadi6022
    @abdelhadi6022 5 лет назад +1

    Thank you, awesome video

  • @abyss-kb8qy
    @abyss-kb8qy 4 года назад +2

    God bless you , thanks you so so so much.

  • @lakshman587
    @lakshman587 3 года назад +4

    16:25 My first *Mega Bam!!!*

  • @CC-um5mh
    @CC-um5mh 5 лет назад +1

    This is absolutely a great video. Will you cover why we can use residual/(p*(1-p)) as the log of odds in your next video? Very excited for the part 4!!

    • @statquest
      @statquest  5 лет назад +1

      Yes! The derivation is pretty long - lots of little steps, but I'll work it out entirely in the next video. I'm really excited about it as well. It should be out in a little over a week.

  • @sebastianlinaresrosas3278
    @sebastianlinaresrosas3278 5 лет назад +2

    How do you create each tree? In your decision tree video you use them for classification, but here they are used to predict the residuals (something like regression trees)

  • @AmelZulji
    @AmelZulji 5 лет назад +1

    First of all thank you for such a great explanations. Great job!
    It would be great if you could make a video about the Seurat package, which very powerful tool for single cell RNA analysis.

  • @KalyanGk0
    @KalyanGk0 3 года назад +1

    This guy literally coming to my dreams 😂

  • @sandralydiametsaha9261
    @sandralydiametsaha9261 5 лет назад +1

    thank you very much for your videos !
    when will you post the next one ?

  • @timothygorden7689
    @timothygorden7689 2 года назад +1

    absolute gold

  • @joeroc4622
    @joeroc4622 5 лет назад +1

    Thank you very much for sharing! :)

  • @nicholas_as
    @nicholas_as 3 года назад

    15:42 What class should be classified if the probability is exactly 0.5 since 0.5 is equal to the threshold ?

    • @statquest
      @statquest  3 года назад

      Probably the best thing to do is to return an error or a warning.

  • @叶涵
    @叶涵 4 года назад +1

    very detailed and convincing

  • @krazykidd93
    @krazykidd93 5 месяцев назад +1

    Wow! I haven't seen a Mega BAM before!

    • @statquest
      @statquest  5 месяцев назад

      :) That was for a special friend.

  • @pranaykothari9870
    @pranaykothari9870 5 лет назад +2

    Can GB for classification be used for multiple classes? If yes, how will the math be, the video explains for binary classes.

  • @jayyang7716
    @jayyang7716 2 года назад +1

    Thanks so much for the amazing videos as always! One question: why the loss function for Gradient Boost classification uses residual instead of cross entropy? Thanks!

    • @statquest
      @statquest  2 года назад

      Because we only have two different classifications. If we had more, we could use soft max to convert the predictions to probabilities and then use cross entropy for the loss.

    • @jayyang7716
      @jayyang7716 2 года назад

      @@statquest Thank you!