AdaBoost : Data Science Concepts

How do we put together lots of weak models into a STRONG model?

[@rmiliming]

I really like how you can clearly explain complicated concepts in as short as 10 mins and just over one whiteboard ! Much better than some other videos which tend to be long and take multiple slides. We are blessed to have you on KZbin!

[@michaltrodler195]

Finally! One man on the whole KZbin elucidated the subject with eloquence, demonstrating a remarkable command of the English language and skillful execution in his presentation. Deserved a subscribe 🎉

[@Matt-tn2on]

Man, I really love your videos. I live overseas and it’s always the first thing I watch when I wake up. Thank you for the clean explanations.

[@hameddadgour]

I finally understood the difference between bagging and boosting by watching your videos. Thank you!

[@drsandeepvm5622]

Highly involved in your explanation, the comment diverted me to realise the mic wire problem. We're here for the great and simple explanation for any concept of any complexity you provide. Thanks for everything.

[@tapanbagchi3835]

Ritvik, you are just superb! I am from IIT Kharagpur and I love your crystal clear teaching - Tapan Bagchi

[@parhambateni]

Thanks for your great power in clarifying these concepts.

[@oskeeg619]

Thank you!! I now have a clear understanding of AdaBoost and can have conversation about it.

[@evgenyivanko9680]

you are doing a great job

[@sihatafnan5450]

That was great explanation

[@ritvikmath]

Glad it was helpful!

[@williamfriedeman7078]

Clear explanation, thank you!

[@hahahaYL-h3x]

Thanks!

[@riccardobellide6975]

Thak you for these videos, so useful

[@jijie133]

Great video!

[@talaasoudalial-bimany6605]

Thank you for the Good and Clear Explanation; I request a stacking generalisation algorithm video if possible.

[@jaivratsingh9966]

excellent

[@thomaspavelka7335]

Thanks a lot!

[@ritvikmath]

You're welcome!

[@lancelotdulak1640]

Very clear and useful !

[@ritvikmath]

Glad it was helpful!

[@gemon39]

Amazing video! Could you please explain XGBoost ?

[@ritvikmath]

good suggestion!

[@ajithshenoy5566]

@@ritvikmath +1 for xgboost please

[@hahahaYL-h3x]

Just a quick comment. Your teaching is brilliant! I learn a lot from you. But I found the organization of topics (the order in the data concept stream) is a little bit confusing.

[@OneworldKW]

I need to practice 😮

[@verayan2019]

omg, so well explained

[@ayseyesil1532]

Why did you draw different lengths for 3 and 4? Isn't in the first step a correct prediction yield error of exp(-alpha1) and wrong prediction yield exp(alpha1)? So the correct ones should have the same size, incorrect ones should have larger but same size. And how do you choose alpha values?

[@jarrelldunson]

Thanks again for a great video. How do you work with scaling alpha constants? a1, a2? How do you recognize which steps need adjusting (e.g., increasing/decreasing the alpha constants, a1, a2, etc.)? Or is this over-fitting?

[@TheMarComplex]

Thank you!

[@ritvikmath]

No problem!

[@kevinscaria]

Can someone explain, how is the value of alpha set? In the original implementation it was the log of errors, but since this looks like a generalized formula, is alpha a hyper parameter?

[@thespicycabbage]

Thx bro!

[@ritvikmath]

No problem

[@Lucifer-wd7gh]

Brother I request , please make a video on gradient decent 🙏

[@Donaid]

Thank you for the video

[@bhargav8232]

Thank you

[@yannickpezeu3419]

Thanks

[@n.m.c.5851]

Should have started commenting for Support a long time Ago

[@emuccino]

Fastest subscribe

[@ccuuttww]

Did he just miss how alpha made?

[@kdhlkjhdlk]

Is adaboost even used for anything anymore, with random forest and gradient boosting being so much better?

[@sansin-dev]

Only to pass exams.

[@phantinh2613]

What if I have 3 results now? help

[@__-de6he]

But second learner can correct the previous mistake but make your own. How is this ploblem resolved?

[@minhaoling3056]

You can tune alphas

[@__-de6he]

@@minhaoling3056 I don't know what is "alphas" 🙂

[@cleansquirrel2084]

Thank you

[@cleansquirrel2084]

Best thumbnail till now

[@brave_v]

Thank you for this awesome video! Is my understanding correct: Gradient boosting assigns all weights to different errors but adaboost assigns different weights to different errors? I'm also confused why Adaboost just uses the exponential loss function where the gradient boosting you can choose whatever loss function you want?

[@elihaylevi6569]

great contant thanks you!