This question was asked in my interview. 7 mins of this video changed my life, 5 years ago! Thank you

Took a convex optimization course last year. you explained clearly in 3 videos, what took days of digging previously. Papa Bless

Excellent job explaining Ridge and Lasso. Your equations/functions AND visuals close the loop nicely!

First time, I am exploring the meaning of LASSO Regression and I have no confusion after watching this video. Very helpful. Thanks Ritvik Kharkar.

The best introduction of LASSO, very easy to understand! Thanks!

your videos are extremely helpful and easily to understand the math behind ML! Thanks a ton!

You are awesome. Thank you for your passion to teach this topic!

Amazing clarity of idea...and perfect speed for the explanations :D

thanks for the video so much. I've been confused by dumb prof for almost one year, and until I understand ridge and l1l2 penalty just by a video. thank you

Falling in love with your videos

Excellent video. Very clear. Thank you.

solid video. saved my interest in the subject, so thank you very much!

It's so clearly and very helpful !! Thank you so much !

Brilliantly explained - Brandon Foltz love you video series!!!!

very helpful! Like the speed you speak

@michaelfresco2769

5 жыл бұрын

also exceptionally clear

@glaswasser

3 жыл бұрын

i first thought I had youtube still on 1.5 speed haha

This insane Man. Thank you so much.

finally! Stumbled upon that figure in the ISLR book but did not understand what was going on, you made it clear to me now, thanks!

@ritvikmath

3 жыл бұрын

Glad I could help!

Most perfect Video on this stuff.. Even the pace was something I could keep with :)

This is the best explanation for lasso vs main OLS in YT! Thanks

Very clear explanation of the contour!

Awesome introduction, thanks! Keep posting videos!

Thank you so much !! the explanation is so good !

Super precise and incredibly helpful!!

Thank you so much for sharing this brilliant video! If you can afford it, I hope you cover the unique feature of adaptive lasso (oracle properties) too.

really succinct and to the point. good explanation

So great! Thank YOU!

very helpful, thanks very much Ritvik!

Very good explanation, thanks a lot !

Awesome Video. Thanks a million for upload :-)

thanks a lot...God Bless!!

Thanks, very informative

perfectly expplained. Thank you so much

really good explanation!

Awesome Job!

Thank you!!!

great explanation!

Incredibly good.

Thanks, cheers!

Thanks for the Video!

Thank you! A very helpful video. Please consider making a video on coordinate descent. :)

outstanding

Brilliant!!

Thanks!

very nice video!

awesome video !!!

Thanks it really helped.

Good job !

GREAT VIDEO! :D

good explanation

Awesome!

Great videos as expected 😊.. Also please find time to make videos on: - A/B testing - Survival Modelling - Type of errors - GBM

@ritvikmath

4 жыл бұрын

Thanks! And I will look into those suggestions

beast lecturer !

thank you!

@ritvikmath

4 ай бұрын

Welcome!

excellent

thx for doing this video, intuitively helpful! couple of questions, 1) In lasso, are resultant coefficients be always positive or zero? 2) do we still interpret coefficients after they get penalized by whatever lamda value we pass?

Hey there, Thanks for all the explanation. Could you make a video on Non-Linear Least Square (NLS) estimator and how is it different from OLS? Thanks

thanks. it's helpful

great stuff man, you should put up a course on udacity or something !!

Can you please explain again why exactly the co-efficients of the B vector hit the edges of the pyramid in case of Lasso Regression, but they do not hit the circumference in case of Ridge Regression. This is the only concept I am not being able to grasp that how does Lasso lead to elimination of co-efficients, but Ridge only causes shrinkage of co-efficients and not entire deletion.

Hello! I have a question related with this video and with the Ridge Regression video. Why shoud not I use these methods if I have one variable ( Y = betha_0 + betha_1*X) ? What would happend if I used one of these methods in that situation? Thank you!

this intuitive explanation made lasso regression "click" by me, so a big thanks! Were you inspired / did you get the ideas / diagrams from a book or did you come up with them yourself?

Thank you so much! So how do you come up with a suitable value for c?

I have been learning about data science from the last 6 months but there is no article or no videos that are better than users.

赞！

could you please explain how some of the coefficients are becoming ZERO in LASSO? I would like to know the internals.

if LASSO is for feature selection how it's different from PCA? Pls clarify

What is the meaning of green level curves, why are they used ?

Thanks for the video. Explaination is really great. But I have a question, what if the curve passes through line between (c,0) and (0,c) and also between (c,0) and (0,-c) , then which point would be better?

@harminderpuri1243

6 жыл бұрын

that curve would not be the smallest curve .. there will be curves with lower (y - Bs)^2 .. plot it and visualize

what is its optimization formular?

Why do the corners get hit a lot more than other points?

How do you pick the constraint c?

Hello! So will C be 25 in the case? (around 5 mins)

it's very good, but too fast!!!

great explanation, but it's a little bit misleading, since we are not regul. our beta0, but beta1..m.

@ritvikmath

4 жыл бұрын

You have a good point, thank you!

So finding new beta is done by taking derivative of lasso formula with respect to beta? And subtracting it from old beta?

I just don't agree with you in the feature selection argument, if beat comes with many zeros that doesn't mean the model is conducting any feature selection process there, it will automatically ignore the zeros. Probably feature selection is something different.

Awesome, so what exactly are the "betas"?

@thestyxx

8 жыл бұрын

+Josh Espinoza The regression coefficients, in other words, the estimated effects of your parameters.

Great explanations. Just wish you would talk a tad bit slower.

beta_0 should not be in the regularization term.

@akhileshpandey8457

7 жыл бұрын

he is just using it as an example.. he explained that in Ridge video

This is a great video. I suggest you do NOT touch or move the piece of paper this much. It'll be less distracting and help the audience look at equations and compare the information.

Thank you!!!

outstanding