Gradient Boost Machine Learning|How Gradient boost work in Machine Learning

[@prasaddalvi3017]

By far the best theoretical explanation on Gradient Boosting. Now I am very much clear on how Gradient Boosting works. Thank you very much for this detailed explanation

[@UnfoldDataScience]

Thanks Prasad.

[@arjunp3574]

This is the most simplified explanation of gradient boosting I've come across. Thank you, sir.

[@UnfoldDataScience]

Glad it was helpful Arjun.

[@denial4958]

Thank you sir it's the day before my exam and this concept was very unclear to me no matter how much I researched. Simply a life saver👏👏

[@shashankbajpai5659]

by far the simplest and the best explanation i could have for adaboost

[@UnfoldDataScience]

Thanks Shashank. Happy Learning. Stay Safe. tc

[@callingTardis]

This channel is my Bible!! Thank you for creating ML content, Aman Sir

[@UnfoldDataScience]

Your comments are precious.

[@sriramapriyar6745]

I have no words to thank you for teaching this complex concept in a simple and effective way. My heartfelt thanks and keep going with the same spirit.

[@UnfoldDataScience]

Hello Sri, Thanks for you words. These are my motivation to create more content. Happy Learning. Tc :)

[@warpathcucucu]

mate, that's literally the best explanation for this topic on youtube

[@UnfoldDataScience]

Wow, thanks a lot.

[@hvs147]

This is by far the clearest/best explanation on Gradient Boosting. Thanks man. God bless!

[@UnfoldDataScience]

Thanks Harsha. Happy Learning. Tc

[@Atlas-ck9vm]

Probably the greatest explanation of gradient boosting on the internet.

[@UnfoldDataScience]

Thanks a lot.

[@GopiKumar-ny3xx]

Nice presentation.... Useful information

[@UnfoldDataScience]

Thanks a lot :)

[@sandeepnayak2427]

Its excellent, very much clearly step by step explained , Highly Appreciable ...You are Awesome ..

[@UnfoldDataScience]

Thanks Sandeep. Keep larning. Stay Safe !

[@preranatiwary7690]

Gradient boost is clear now! Thanks.

[@UnfoldDataScience]

Thank you Prerana:)

[@praveenkuthuru7439]

Great work !!! Really helps a common person to learn about the GB Algorithm in action in simple terms....Keep up your good work !!!!

[@UnfoldDataScience]

Thanks Praveen, will do!

[@gg123das]

Best Gradient Boosting video on RUclips!!!!

[@UnfoldDataScience]

Glad it was helpful Jafar. Happy Learning. Tc

[@divyanshumnit]

Thank you so much, Sir. I have watched it so many places but the clarity I got from your video. just watching this video I subscribed to your channel.

[@UnfoldDataScience]

Thanks Divyanshu.

[@praneethaluru2601]

Very goood and elegant explanation of GBoost than others on RUclips Sir...

[@UnfoldDataScience]

Thanks For watching Praneeth.

[@divyosmiley9]

Thank You, Sir. I read many papers but was so confused, but you made it clear.

[@UnfoldDataScience]

Thanks for your valuable comment.

[@IRFANSAMS]

@Unfold Data Science, Sir the way you explain complex topics in a simple manner is extraordinary

[@kayodeoyeniran2862]

Thank you for demystifying such a confusing concept. This is the best explanation by far!!!

[@UnfoldDataScience]

Thank you

[@OhSohomemade]

Hey Aman ..very well explained ... I am beginner and was looking for a easy and practical way of learning these concepts and you made it easy ..thanks very much ..appreciate the good work ..cheers

[@UnfoldDataScience]

It's my pleasure. Keep Learning. Stay Safe. tc :)

[@RanjitSingh-rq1qx]

Super explanation with in less time. With mathmatics intuition. Tnq u sir for made this mind-blowing video ❤️🥰😊

[@amalaj4988]

Learning a lot from you sir! Crisp and clear points as usual :)

[@UnfoldDataScience]

Thanks Amol. Your comments motivate me to create more content 😊

[@dd1278]

Legend you are for explaining this so simply.

[@UnfoldDataScience]

Thanks again deb.

[@adithyaboyapati]

Explanation is crisp and very clear.

[@UnfoldDataScience]

Thanks Adithya.

[@vivekbhatia8230]

Very nicely explained sir.. as u said it was not very clear in net.. after your explanation i can understand the working of the gradient boost model.

[@amithnambiar9818]

Thank you ! Never seen a video so detailed yet understandable about Gradient Boosting

[@UnfoldDataScience]

Thanks Amith. Happy Learning. Tc

[@sudhavenugopal3726]

Well explained, where a beginner can understand this, thank you so much

[@UnfoldDataScience]

Thanks a lot Sudha :)

[@FarhanAhmed-xq3zx]

Very very simple and clear explanation.really awesome👌👌

[@UnfoldDataScience]

Thanks Farhan.

[@josephmart7528]

You have made my day with this Ensemble Explanations

[@UnfoldDataScience]

Thanks Joseph.

[@cedwin4]

Simple and best. Speechless! Thanks a lot :)

[@naqiuddinadnan9156]

I need to study this by myself but mostly the explaination are not soo clear but u give great explaination 👍🏼👍🏼👍🏼

[@UnfoldDataScience]

Thanks Naqiuddin :)

[@dorgeswati]

you are awesome . video shows the depth you have in understanding these algorithms well. keep it up

[@UnfoldDataScience]

Thanks a lot!

[@krishnab6444]

thats a perfect explanation aman sir, in a simplest way, thanks alot sir, your videos are really helpful.

[@UnfoldDataScience]

So nice of you Krishna

[@shashireddy7371]

Thanks for sharing your knowledge with great explanation .

[@UnfoldDataScience]

Welcome shashi, keep learning :)

[@snehasivakumar9542]

Easy to understand. 😊👍

[@UnfoldDataScience]

Thank you Sneha :)

[@bangarrajumuppidu8354]

superb explanatio fanstastic!!

[@UnfoldDataScience]

Thanks Bangarraju.

[@tejaspatil3978]

Sir , it is very really best and very easiest explanation.
Wait for more videos

[@UnfoldDataScience]

Keep watching Tejas. Happy Learning.

[@sarthakgarg184]

I have been searching for a better intuition on Gradient Boosting and this is the first video which gave me the best intuition. I am looking for research projects, can you help me with some topics on Machine Learning and Deep Learning which I could explore and ultimately go for a paper!
I'm also reaching out to you on LinkedIn for better reach. Thankyou for the video :)

[@UnfoldDataScience]

Thanks Sarthak, lets connect on LinkedIn and we can discuss more. Stay Safe. Tc.

[@KenzaLamnabhi-f8l]

Thank you for this video! really amazed by how you siplify complex concepts !
Keep them going please!

[@hashir3719]

It's crystal clear mahn..! thank you

[@anilboppanna]

Very nicely explained keep posting on such a quality videos..to unfold the data science Black box

[@UnfoldDataScience]

Thanks Anil. Happy Learning. Keep watching :)

[@abiramimuthu6199]

I was running around so many videos for Gradient boosting.........Than you so much for your detailed explanation.....How does it work for a classsification problem?

[@UnfoldDataScience]

Hi Abirami, thank you for the feedback. It's difficult to explain the classification problem through comment. I ll probably create a video for the same :)

[@shubhankarde4732]

please create one video for classification as well.....

[@srinikethshankar7525]

At 4:00, the error for the OLS method for linear regression is the vertical distance between the regression line and the data points and not the orthogonal distance between them. :)

[@UnfoldDataScience]

Oops Did I say "orthogonal". I should be vertical. Thanks for pointing that out. happy learning. tc

[@Sagar_Tachtode_777]

Thank you for sharing such a piece of valuable knowledge in free.
May God bless you with exponential growth in the audience and genuine learners!!!

[@UnfoldDataScience]

So nice of you Sagar. Thanks for motivating me through comment.

[@jude-harrisonobidinnu3876]

Very amazing videos. Concepts worth more than jumping into codes. Well done Sir!

[@ChengchengCai-v9k]

Thank you! Best explanation! I can understand it!

[@mansibisht557]

Best video so far ! :') Thank you!!!

[@UnfoldDataScience]

Welcome Mansi.

[@MinaGholami-e2u]

Thank you. it was perfect explanation of gradient algorithm

[@UnfoldDataScience]

Glad it was helpful!

[@tradingdna5706]

Best teacher 👍🏻

[@IRFANSAMS]

Aman sir, Allah will give you more success in your life

[@UnfoldDataScience]

Thanks Imran, your comment mean a lot to me.

[@saravananbaburao3041]

One of the best video that I have ever watched for GB . Thanks a lot for the video. Can you please cover one video on Bayesian optimization . Really I find difficult to understand on that topic . Thanks in advance

[@UnfoldDataScience]

Noted.

[@shikhar_anand]

Hi Aman, Thank you very much for the video. It was by far the clearest explanation for the topic. Just one doubt if you could clear it, How we can decide the number of iterations for any problem? You have iterated this for n=2, so how we can decide that.

[@UnfoldDataScience]

Hi Shikhar, We can pass it as parameter while calling the function.

[@adityasharma2667]

very well explained...i could say the best video to understand GB

[@UnfoldDataScience]

Glad it was helpful Aditya.

[@poetryspeculation2344]

Brilliant videos man! Thank you so much!

[@sachinmore8938]

You have got very good explanation skills!

[@ricardocaballero6357]

This is awesome, excellent explanation, thanks a lot

[@sandhya_exploresfoodandlife]

hi Aman.. your explanations are so good! thanks a lot

[@UnfoldDataScience]

Thanks Sandhya.

[@eric_bonucci_data]

Super clear, thanks a lot!

[@UnfoldDataScience]

Welcome Eric.

[@19967747]

Very well explained ! Please keep on making such nice videos !
Hope you reach 100k subscribers soon

[@UnfoldDataScience]

Thank you Ajinkya. Keep watching :)

[@parthsarthijoshi6301]

in gradient descent I read that new parameter = old parameter - step size but in your example of Gradient boosting you have done base value + (lr* 1st res) . IS THE + CORRECT????

[@UnfoldDataScience]

Hi Parthsarthi, yes step size has same meaning.

[@parthsarthijoshi6301]

Sir , I mean to say that you have done addition not subtraction . Is that correct?

[@sharmita220]

Thank you so much for all the videos. Its so clear

[@vithaln7646]

this is very clear explanation ,

[@UnfoldDataScience]

Thanks Vithal. Happy Learning. Stay Safe!!

[@pranjalgupta9427]

Awesome video 😍😍

[@UnfoldDataScience]

Thanks Pranjal. Hope you are doing great.

[@shubhankarde4732]

great explanation...liked a lot

[@UnfoldDataScience]

Thanks a lot.

[@peaceandlov]

Super Awesome mate.

[@UnfoldDataScience]

Thanks for the visit

[@sameerpandey5561]

Thank you Aman..It was very crisp and clear...explanation
Just a request..Please add a Video explaining GBDT in case of classification problems ...That would be very much helpful :-)

[@UnfoldDataScience]

I will try my best.

[@firstkaransingh]

Very good and clear explanation 👍

[@alealejandroooooo]

This was great man, thanks!

[@UnfoldDataScience]

Thanks a lot.

[@mdshihabuddin4099]

Thanks a ton for your spotless explanation . I have a question, how many residual models we will compute for getting our expected model, or how we will understand we need to compute that much of residual model.

[@UnfoldDataScience]

Good question Shihab, that number is a hyperparameter that can be tuned however there will be some default value for algorithm in R and Python.

[@mdshihabuddin4099]

Thanks for your response.

[@preetibhatt5085]

Great explanation ... u said it right , couldn’t find right material for boosting on net . Could u pls make a video on XGBoost as well ??thanks for ur response in advance

[@UnfoldDataScience]

Sure Preeti.

[@MohitGupta-sz4bh]

How the algorithm decides the no of trees in Gradient boosting. And its advantages and disadvantages over Adaptive boosting. When to choose what... Please explain or reply in comments and yours videos are very helpful for someone like me who wants to switch his Career in Data Science field.
Also Can you please explain why we have the leaf nodes in the range of 8-32 in Gradient boosting and only one leaf node in Adaptive Boosting.

[@UnfoldDataScience]

# of trees - u can pass as parameter
AdaBoost vs GB which to choose - depends on scenario
I dont think there will be only one leaf node

[@kalluriramakrishna5732]

excellent aman. Thank you so much

[@ranajaydas8906]

The best explanation I ever saw!

[@UnfoldDataScience]

Glad you liked it. Continue watching

[@MbaliNene-kf6wk]

Thank you so Aman this really helps!

[@nurulamin7982]

Awesome.

[@UnfoldDataScience]

Thanks Nurul

[@jagannadhareddykalagotla624]

@aman how to choose learning rate value and how to choose no.of trees

[@subhajit11234]

If you keep on growing the trees, it will overfit. How do you stop that? Will the model automatically stop ? or do we need to tune the hyperparameters? Also, it will be helpful if you can pick a record which we want to predict after training and demonstrate what will be the output, then that will be good. Going by your theory, all records you want to predict will have the same prediction. :)

[@UnfoldDataScience]

Hi Suvajit,
We must prune decision tree to avoid over fitting. Pruning can be done in multiple ways, like limiting number of leaf nodes, limiting branch size, limting depth of tree etc.
All these inputs can be passed to model when we call gradient boost. For optimal values, we should tune the hyper parameter.
Coming to part 2 of the question, all the records will not have the same prediction as error is getting optimized in every iteration. In the same model, If i try to predict for two different records, predictions will be different based on value of independent columns.

[@himanshupathak3090]

Great explanation on gradient boosting.... your videos are making look such complex algorithms so easy :)

[@UnfoldDataScience]

Glad you like them Himanshu.

[@himanshupathak3090]

Unfold Data Science can you also make a video on Support Vector Machines

[@aiuslocutius9758]

Thank you very much. Learning a lot from your videos!

[@UnfoldDataScience]

Welcome.

[@abhishekdas2640]

Hello sir,
So I have a doubt,
which type of model we are using while predicting New Residuals. (Is it simple Linear Regression or any thing else)

[@UnfoldDataScience]

Decision trees.

[@abhishekdas2640]

@@UnfoldDataScience Thank you sir. It would be great if I can see it practically with an example. But the video is really helping me out to understand the core concepts. ❤️❤️❤️

[@shreyaanand6323]

simple and best

[@bhargavdr]

Twas very helpful thank you.

[@UnfoldDataScience]

Welcome Bhargav.

[@surajsthomas]

Awesome video.Very well explained.

[@UnfoldDataScience]

Thanks Suraj.

[@SuperShiva619]

Ur awesome🙂 the intent at which u explain concepts are mind blowing.

[@UnfoldDataScience]

Thaks Satish, keep watching :)

[@subhz1]

Excellent video!!! Great work!!!!

[@UnfoldDataScience]

Thank you very much Subhadip :)

[@Gamezone-kq5sx]

best explanation....good going

[@UnfoldDataScience]

Thank you 🙂

[@ajaykushwaha-je6mw]

Thank you so much for such a nice video. It help me to understand thee concept of GB Algo.

[@UnfoldDataScience]

Glad it was helpful Ajay. Happy Learning!

[@bhushanchaudhari378]

Very well explained sir🎂.. thanks a ton

[@UnfoldDataScience]

Welcome Bhusan. Keep watching :)

[@RaviShankar-jm1qw]

Awesome and super clear explanation. :)

[@UnfoldDataScience]

Glad it was helpful!

[@hirdeshkumar4069]

How will you define you learning rate and how did you arrive to the value 0.1?

[@UnfoldDataScience]

This is just a number I took for explanation however this is a parameter that can be tuned..

[@chaitanyakaushik6772]

Excellent explaination sir.

[@UnfoldDataScience]

Thanks and welcome

[@sudheerrao9820]

Thank you for the video...very useful

[@UnfoldDataScience]

Welcome Sudheer, keep watching :)

[@goelnikhils]

Amazing Content. Thanks a lot

[@UnfoldDataScience]

Welcome Nikhil,pls share with friends

[@dimitarnentchev1107]

Very clearly explained ! Thank you.

[@UnfoldDataScience]

Glad it was helpful Dimitar!

[@devikasathyan1333]

simply super sir . you are indeed a life saver :)

[@UnfoldDataScience]

Thanks Devika, Good to know my efforts are helping you. Happy Learning. Stay Safe. tc

[@devikasathyan1333]

@@UnfoldDataScience yes ofcourse... I was searching for the same ... And only your video has got the correct content🙌😇

[@pokabhanuchandar9140]

Hi aman thanks for explaining the concepts. here I have one question for u "will ada boost accept repetitive records like random forest? "

[@prasadjayanti]

good work..

[@UnfoldDataScience]

Thanks a lot.

[@soumyagupta9301]

I understood how Gradient Boosting works but still not understood why it works. Actually, I am not getting the intuition behind why we are interested in training the model on the residual error rather than the true value of y. Can you please explain this in a bit more detail? Anyway, I am a big fan of your teaching.,.it's so simple and easy to understand. Thank you for teaching so well.

[@UnfoldDataScience]

Thanks Soumya. you work with data more and you will know.

[@nikkitha92]

This video is very awesome sir. I was able to understand very well.

[@UnfoldDataScience]

Thanks a lot Nikkitha :) Stay Safe, Keep Learning !

[@ymaheshwarreddy]

Kudos!! Informative videos. "Data Science made Easy" is what i have to say:)

[@UnfoldDataScience]

Thanks Mahesh. Keep watching :)