Gradient Boost Part 3 (of 4): Classification
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- Опубликовано: 19 июн 2024
- This is Part 3 in our series on Gradient Boost. At long last, we are showing how it can be used for classification. This video gives focuses on the main ideas behind this technique. The next video in this series will focus more on the math and how it works with the underlying algorithm.
This StatQuest assumes that you have already watched Part 1:
• Gradient Boost Part 1 ...
...and it also assumed that you understand Logistic Regression pretty well. Here are the links for...
A general overview of Logistic Regression: • StatQuest: Logistic Re...
how to interpret the coefficients: • Logistic Regression De...
and how to estimate the coefficients: • Logistic Regression De...
Lastly, if you want to learn more about using different probability thresholds for classification, check out the StatQuest on ROC and AUC: • THIS VIDEO HAS BEEN UP...
For a complete index of all the StatQuest videos, check out:
statquest.org/video-index/
This StatQuest is based on the following sources:
A 1999 manuscript by Jerome Friedman that introduced Stochastic Gradient Boost: statweb.stanford.edu/~jhf/ftp...
The Wikipedia article on Gradient Boosting: en.wikipedia.org/wiki/Gradien...
The scikit-learn implementation of Gradient Boosting: scikit-learn.org/stable/modul...
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Кино
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 book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
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.
@@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.
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.
@@jonelleyu1895 Yes, even for classification, the target variable is continuous (probabilities instead of Yes/No), and thus, we use regression trees.
I cannot imagine the amount of time and effort used to create these videos. Thanks!
Thank you! Yes, I spent a long time working on these videos.
Love these videos! You deserve a Nobel prize for simplifying machine learning explanations!
Wow, thanks!
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.
bam!
@@statquest Double Bam!
Bam?
This content shouldn’t be free Josh. So amazing Thank You 👏🏽
Thank you very much! :)
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)
Awesome! :)
The best explanation I've seen so far. BAM! Catchy style as well ;)
Thank you! :)
@@statquest are the individual trees which are trying to predict the residuals regression trees?
@@arunavsaikia2678 Yes, they are regression trees.
you really explain complicated things in very easy and catchy way.
i like the way you BAM
BAM!!! :)
Thanks for all you've done. You know your videos is first-class and precision-promised learning source for me.
Great to hear!
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"
That's an excellent lesson and a unique sense of humor. Thank you a lot for the effort in producing these videos!
Glad you like them!
Will recommend the channel for everyone study the machine learning :) Thanks a lot, Josh!
Thank you! :)
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.
Glad it was helpful!
Love these videos. Starting to understand the concepts. Thank you Josh.
Thank you! :)
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 !!
Thank you! :)
I'm new to ML and these contents are gold. Thank you so much for the effort!
Glad you like them!
Thank you Josh for another exciting video! It was very helpful, especially with the step-by-step explanations!
Hooray! I'm glad you appreciate my technique.
Yet again. Thank you for making concepts understandable and applicable
Thanks!
Fantastic video , I was confused about the gradient boosting, after watching all parts of gb technique from this channel, I understood it very well :)
Bam! :)
Already waiting for Part 4...thanks as always Josh!
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.
Finally a video that shows the process of gradent boosting. Thanks a lot.
Thanks!
Thank you so much for this series, I understand everything thanks to you!
bam! :)
I wish I had a teacher like Josh! Josh, you are the best! BAAAM!
Thank you!:)
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!!!!!!”
AWESOME!!!
Amazing and Simple as always. Thank You
Thank you very much! :)
Absolutely wonderful. You are are my guru and a true salute to you
Thank you!
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.
I have beeeeennnn waiting for this video..... Awesome job Joshh
Thanks!
Excellent as always! Thanks Josh!
Thank you! :)
This is amazing. This is the nth time I have come back to this video!
BAM! :)
Hi Josh, great video.
Thank you so much for your great effort.
Thank you!
All your videos are super amazing!!!!
Thank you! :)
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!!
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.
man, you videos are just super good, really.
Thank you!
Superb video without a doubt!!!
one query Josh, do you have any plans to cover a video on "LightGBM" in near future?
amazing as always !!
Any time! :)
thanks alot , ur videos helped me too much, plz keep going
Thank you!
Great video! Thank you!
Thanks!
Thank you very much for sharing! :)
Thanks! :)
I was wrong! All your songs are great!!!
Quadruple BAM!
:)
Your are very helpful, thank you!
Thank you!
Great videos again! XGBoost next? As this is supposed to solve both variance (RF) & bias (Boost) problems.
Can GB for classification be used for multiple classes? If yes, how will the math be, the video explains for binary classes.
Hi Josh thanks alot for your clearly explained videos. I had a question @12.17 when you make the second tree spliting the tree twice with Age only the node and the decision node both are Age. If this is correct will not be a continuous variable create kind of biasness? My second question when we classify the the new person @ 14.40 the initial log(odds) still remains 0.7? Assuming this is nothing but your test set however what happens in the real world scenario were we have more records does the log odds changes as per the new data we want to predict meaning the log of odds for train and test set depends on their own averages (the log of odds)?
Hi Statquest would you please make a video about naive bayes? Please it would be really helpful
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!
Thank you very much!
Hey Josh,
I really enjoy your teaching. Please make some videos on XG Boost as well.
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
Thank you for good videos!
Thanks! :)
Super Cool to understand and study, Keep Up master..........
Thank you!
Another superb video Josh. The example was very clear and I’m beginning to see the parallels between the regression and classification case.
One key distinction seems to be in calculating the output value of the terminal nodes for the trees.
In the regression case the average was taken of the values in the terminal nodes (although this can be changed based on the loss function selected). In the classification case it seems that a different method is used to calculate the output values at the terminal nodes but it seems a function of the loss function (presumably a loss function which takes into account a Bernoulli process?).
Secondly we also have to be careful in converting the output of the tree ensemble to a probability score. The output is a log odds score and we have to convert it to a probability before we can calculate residuals and generate predictions.
Is my understanding more or less correct here? Or have I missed something important? Thanks again!
You are correct! When Gradient Boost is used for Classification, some liberties are taken with the loss function that you don't see when Gradient Boost is used for Regression. The difference being that the math is super easy for Regression, but for Classification, there are not any easy "closed form" solutions. In theory, you could use Gradient Descent to find approximations, but that would be slow, so, in practice, people use an approximation based on the Taylor series. That's where that funky looking function used to calculate Output Values comes from. I'll cover that in Part 4.
Thank you so much. Great videos again and again.
One question, what is the difference between xgboost and gradient boost?
please reply @statQuest team
Thank you, awesome video
Thank you! :)
Thank you for sharing this Josh. I have a quick question - the subsequent trees which are predicting residuals are regression trees (not classification tree) as we are predicting continuous values (residual probabilities)?
Yes
You r amazing sir! 😊 Great content
Thanks a ton! :)
nice explanation and easy to understand thanks bro
You are welcome
thank you very much for your videos !
when will you post the next one ?
Do we have a video on neural network? It seems to me we just throw a bunch of functions and get an output. What is the idea of it? Why does it work at all?
Very helpful explanation. Can you also add a video on how to do this in R? Thanks
Now I want to watch Troll 2
:)
Somewhere around the 15 min mark I made up my mind to search this movie on google
@@AdityaSingh-lf7oe bam
thanks for videos. best of anything else I did see. Will use this 'pe-pe-po-pi-po" as message alarm on phone)
bam!
This is great!!!
Thank you! :)
Simply Awesome!!!!!!
Thank you! :)
Respect and many thanks from Russia, Moscow
Thank you!
Another great lecture by Josh Starmer.
Hooray! :)
@@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.
@@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/
@@statquest I will!
Hi Josh, great video as always! Can you explain to me or recommend a material to understand the GB algorithm when we are using it for a non-binary classification? E.g. we have three or more possible outputs for classification.
Unfortunately I don't know a lot about that topic. :(
very detailed and convincing
Thank you! :)
God bless you ， thanks you so so so much.
Thank you! :)
I salute your hardwork, and mine too
Thanks
@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?
Each time we build a new tree.
Fantastic song, Josh. I have started picturing that I am attending a class and the professor/lecturer walks by in the room with the guitar, and the greeting would be the song. This could be the new norm following stat quest. One question regarding gradient boost that I have is why it restricts the size of the tree based on the number of leaves. What would happen if that restriction is ignored? Thanks, Josh. Once again, superb video on this topic.
If you build full sized trees then you would overfit the data and you would not be using "weak learners".
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?
We use regression trees, which are explained here: ruclips.net/video/g9c66TUylZ4/видео.html
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)
same question
How does the multi-classification algorithm work in this case? Using one vs rest method?
It's been over 11 months and no reply from josh... bummer
have the same question
@@AnushaCM well, we could use one vs rest approach
It uses a Softmax objective in the case of multi-class classification. Much like Logistic(Softmax) regression.
Listening to your song makes me thinking of Phoebe Buffay haha.
Love it, anyway !
See: ruclips.net/video/D0efHEJsfHo/видео.html
@@statquest Smelly stat, smelly stat, It's not your fault (to be so hard to understand)
@@statquest btw i like your explanation on gradient boost too
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 ?
Gradient Boost uses Regression Trees: ruclips.net/video/g9c66TUylZ4/видео.html
Bloody awesome 🔥
Thanks!
@StatQuest Thanks for the great content you provide. It's a great explanation of binary-class classification, but how will all this explanation apply to multi-class classification?
Usually people combine multiple models that test class vs everything else.
Congrats!! Nice video! Ultra bam!!
Thank you very much! :)
Best original song ever in the start!
Yes! This is a good one. :)
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.
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.
@@statquest thank you!
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
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"
Hey Josh, When these trees are being built using the variables, how are you determining how to build them? Are you using gini impurity to choose each split as in the decision tree videos? Same question goes for regression trees in gradient boosting vids. Thanks in advance brother!
For both regression and classification problems, 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
You save me from the abstractness of machine learning.
Thanks! :)
You are awesome !!
Thank you!
Hi Josh,
Does the Gradient Boost use GINI Impurity too select the best node to split on or is it split on a random node or does it make use of some other criterion to split the data
Gradient Boost almost always uses Regression Trees because they are fit to the residuals, which are continuous values. Regression Trees are described here: ruclips.net/video/g9c66TUylZ4/видео.html
Thanks for the great video! One question: Why do you use 1-sigmoid instead of sigmoid itself?
What time point in the video are you asking about?
Hey Josh, just trying to clarify how the root node in gradient boosting machine (gbm) is decided (i'm sure different packages/model types differ) compared to random forest? From what I understand is rf uses a random 'mtry' of predictors to choose the root node and then uses gini or entropy to pick the variable and then splits using this method, etc, etc. But how does gradient boosting machines do this? Is it like a regular decision tree where all predictors are available and some statistic is used to choose the best one ? Thanks as always for your awesome videos and have a good one!
Since the trees in gradient boost predict the residuals, which are continuous, and because it doesn't have it's own special type of tree (like xgboost), it uses regression trees. Here's the StatQuest that explains regression trees: ruclips.net/video/g9c66TUylZ4/видео.html
absolute gold
Thank you! :)
the best video for GBT
Thanks!
HI Josh
Great video.
I have a question.
In the classification example for adaboost the misclassified data points were sampled with higher probability in the next iteration of adaboost. This was very clear in adaboost.
Where and how exactly the misclassified points are assigned higher weightage in GBM so that they can be sampled with higher probability in next iteration of GBM ?
The answer to your question is in this video and more details can be found in the follow up: ruclips.net/video/StWY5QWMXCw/видео.html
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
Triple bam! :)
Thank you so much can you please make a video for Support Vector Machines
Agreed!
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!
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.
@@statquest Thank you!
So finallyyyy the MEGAAAA BAMMMMM is included.... Awesomeee
Yes! I was hoping you would spot that! I did it just for you. :)
@@statquest i was in office when i first wrote the comment earlier so couldn't see the full video...
THIS IS A BAMTABULOUS VIDEO !!!!!!
BAM! :)
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 ?
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.
Hello Josh, So i have a little question. How would we make the first leaf if we have more than 2 labels, Because you said to calculate the first leaf we need to do log(odds) but log(odds) can only be done for classification with 2 labels, What would we do if we had more than 2. Do we use One-vs-All classification like we do in Logistic regression or what?
You can do one-vs-all, or change the loss function see the "objective" parameter here: xgboost.readthedocs.io/en/latest/parameter.html
Thanks for the great video. I wonder if the output of each leaf is probability instead of log(odds), would that simply the math a little?
It would actually make it more complicated. This is because probabilities have hard limits at 0 and 1. So this makes adding the output from an unknown number of trees tricky. In contrast, the log(odds) has no limits (we can add values all the way up to positive infinity if we wanted to), and that gives us the flexibility to add as many trees as need to the model.
Thank you so much.
you're super humorous!!
bam!
Best video ever, quick question on building the next tree. Once we have the new residuals, how do we decide the new node for the next tree? Is it still the same as calculating Gini but on the residuals ?
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