Ridge Regression

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[@xavierfournat8264]

This is showing that the quality and value of a video is not depending on how fancy the animations are, but how expert and pedagogue the speaker is. Really brilliant! I assume you spent a lot of time designing that course, so thank you for this!

[@ritvikmath]

Wow, thanks!

[@backstroke0810]

Totally agree. I learn a lot from his short videos. Precise, concise, enough math, enough ludic examples. True professor mind.

[@rez_daddy]

"Now that we understand the REASON we're doing this, let's get into the math." The world would be a better place if more abstract math concepts were approached this way, thank you.

[@garbour456]

good point

[@tzu-chunchen5139]

This is the best explanation of Ridge regression that I have ever heard! Fantastic! Hats off!

[@GreenEyesVids]

Watched these 5 years ago to understand the concept and I passed an exam. Coming back to it now to refresh my memory, still very well explained!

[@ritvikmath]

Nice! Happy to help!

[@nadekang8198]

This is awesome! Lots of machine learning books or online courses don't bother explaining the reason behind Ridge regression, you helped me a lot by pulling out the algebraic and linear algebra proofs to show the reason WHY IT IS THIS! Thanks!

[@siddharthkshirsagar2545]

I was searching for ridge regression on the whole internet and stumbled upon this is a video which is by far the best explanation you can find anywhere thanks.

[@zgbjnnw9306]

It's so inspiring to see how you get rid of the c^2! I learned Ridge but didn't know why! Thank you for making this video!

[@aarshsachdeva5785]

You should add in that all the variables (dependent and independent) need to be normalized prior to doing a ridge regression. This is because betas can vary in regular OLS depending on the scale of the predictors and a ridge regression would penalize those predictors that must take on a large beta due to the scale of the predictor itself. Once you normalize the variables, your A^t*A matrix being a correlation matrix of the predictors. The regression is called "ridge" regression because you add (lambda*I + A^t*A ) which is adding the lambda value to the diagonal of the correlation matrix, which is like a ridge. Great video overall though to start understanding this regression.

[@taareshtaneja7523]

This is, by far, the best explanation of Ridge Regression that I could find on KZbin. Thanks a lot!

[@SarahPourmolamohammadi]

You are the best of all.... you explained all the things,,, so nobody is gonna have problems understanding them.

[@RobertWF42]

Excellent video! One more thing to add - if you're primarily interested in causal inference, like estimating the effect of daily exercise on blood pressure while controlling for other variables, then you want an unbiased estimate of the exercise coefficient and standard OLS is appropriate. If you're more interested in minimizing error on blood pressure predictions and aren't concerned with coefficients, then ridge regression is better. Also left out is how we choose the optimal value of lambda by using cross-validation on a selection of lambda values (don't think there's a closed form expression for solving for lambda, correct me if I'm wrong).

[@surajshivakumar5124]

This is literally the best video on ridge regression

[@q0x]

I think its explained very fast, but still very clear, for my level of understanding its just perfect !

[@BhuvaneshSrivastava]

Your data science videos are the best I have seen on KZbin till now. :) Waiting to see more

[@ritvikmath]

I appreciate it!

[@nikunjgattani999]

Thanks a lot.. I watched many videos and read blogs before this but none of them clarified at this depth

[@charlesity]

Stunning! Absolute gold!

[@wi8shad0w]

seriously!!!

[@yxs8495]

This really is gold, amazing!

[@canernm]

Hi and thanks fr the video. Can you explain briefly why when the m_i and t_i variables are highly correlated , then the estimators β0 and β1 are going to have very big variance? Thanks a lot in advance!

[@lanag873]

Hi same question here😶‍🌫

[@alecvan7143]

Amazing video, you really explained why we do things which is what really helps me!

[@OmerBoehm]

Brilliant simplification of this topic. No need for fancy presentation to explain the essence of an idea!!

[@xwcao1991]

Thank you. I make the comment because I know I will never need to watch it again! Clearly explained..

[@ritvikmath]

Glad it was helpful!

[@TahaMVP]

best explanation of any topic i've ever watched , respect to you sir

[@abhichels1]

This is gold. Thank you so much!

[@mohamedgaal5340]

I was looking for the math behind the algorithm. Thank you for explaining it.

[@ritvikmath]

No problem!

[@cu7695]

I subscribed just after watching this. Great foundation for ML basics

[@ethanxia1288]

Excellent explanation! Could you please do a similar video for Elastic-net?

[@bettychiu7375]

This really helps me! Definitely the best ridge and lasso regression explanation videos on KZbin. Thanks for sharing! :D

[@mikeperez4222]

Anyone else get anxiety when he wrote with the marker?? Just me? Felt like he was going to run out of space 😂 Thank you so much thoo, very helpful :)

[@murraystaff568]

Brilliant! Just found your channel and can't wait to watch them all!!!

[@e555t66]

I don't have money to pay him so leaving a comment instead for the algo. He is the best.

[@akino.3192]

You, Ritvik, are simply amazing. Thank you!

[@Viewfrommassada]

I'm impressed by your explanation. Great job

[@ritvikmath]

Thanks! That means a lot

[@Lisa-bp3ec]

Thank you soooo much!!! You explain everything so clear!! and there is no way I couldn't understand!

[@babakparvizi2425]

Fantastic! It's like getting the Cliff's Notes for Machine Learning. These videos are a great supplement/refresher for concepts I need to knock the rust off of. I think he takes about 4 shots of espresso before each recording though :)

[@aDifferentHandle]

The best ridge regression lecture ever.

[@theoharischaritidis4173]

This really helped a lot. A big thanks to you Ritvik!

[@teegnas]

These explanations are by far the best ones I have seen so far on youtube ... would really love to watch more videos on the intuitions behind more complicated regression models

[@soudipsanyal]

Superb. Thanks for such a concise video. It saved a lot of time for me. Also, subject was discussed in a fluent manner and it was clearly understandable.

[@RAJIBLOCHANDAS]

Excellent approach to discuss Lasso and Ridge regression. It could have been better if you have discussed how Lasso yields sparse solutions! Anyway, nice discussion.

[@nicolasmanelli7393]

I think it's the best video ever made

[@Krishna-me8ly]

Very good explanation in an easy way!

[@nickb6811]

So so so very helpful! Thanks so much for this genuinely insightful explanation.

[@wi8shad0w]

THIS IS ONE HELL OF A VIDEO !!!!

[@mortezaabdipour5584]

It's just awesome. Thanks for this amazing explanation. Settled in mind forever.

[@jhhh0619]

Your explanation is extremely good!

[@nickwagner5173]

We start out by adding a constraint that beta 1 squared + beta 2 squared must be less than c squared, where c is some number we choose. But then after choosing lamda, we minimize F and c ends up having no effect at all on our choice of the betas. I may be wrong but it doesn't seem like c has any effect on our choice of lambda either. I find it strange that we start out with the criteria that beta 1 squared + beta 2 squared must be less than c squared, but the choice of c is irrelevant. If someone can help me un-boggle my mind that would be great.

[@RobertWF42]

Good question - I think it has to do with using the method of Lagrange multipliers to solve the constrained OLS optimization problem. The lambda gets multiplied by the expression in the parentheses at 11:17, which includes the c squared term. So whatever c squared value you choose, it's going to be changed anyways when you multiply by the lambda.

[@yanlinwang5703]

The explanation is so clear!! Thank you so much!!

[@sanketchavan8]

best explanation on ridge reg. so far

[@prabhuthomas8770]

SUPER !!! You have to become a professor and replace all those other ones !!

[@SUBHRASANKHADEY]

Shouldn't the radius of the Circle be c instead of c^2 (at time around 7:00)?

[@abhijeetsingh5049]

Stunning!! Need more access to your coursework

[@Sytch]

Finally, someone who talks quickly.

[@adrianfischbach9496]

Huge thanks!

[@zw7453]

best explanation ever!

[@prateekcaire4193]

It is unintuitive that we are constraining weights(betas) within value c^2, yet the regularization expression does not include the c but rather sum of squared weights. Certainly I am missing something here. Alternatively, why adding a sum of squared betas(or weights) to the cost function help optimize beta that stays within constraint so that betas don't become large and vary across datasets?

[@vishnu2avv]

Awesome, Thanks a Million for great video! Searching you have done video on LASSO regression :-)

[@intom1639]

Brilliant! Could you make more videos about Cross validation, RIC, BIC, and model selection.

[@sachinrathi7814]

Can anyone explain the statement "The efficient property of any estimator says that the estimator is the minimum variance unbiased estimator", so what is minimum variance denotes here.

[@kartikkamboj295]

Dude ! Hats off 🙏🏻

[@HeduAI]

I would trade diamonds for this explanation (well, allegorically! :) ) Thank you!!

[@shiva6016]

simple and effective video, thank you!

[@sagarsitap3540]

Thanks! why lamba cannot be negative? What if to improve variance it is need to increase the slope and not decrease?

[@tamoghnamaitra9901]

Beautiful explanation

[@faeritaaf]

Thank you! Your explaining is really good, Sir. Do you have time to make a video explaining the adaptive lasso too?

[@LossAndWaste]

you are the man, keep doing what you're doing

[@adityakothari193]

Excellent explanation .

[@kamesh7818]

Excellent explanation, thanks!

[@meysamsojoudi3947]

It is a brilliant video. Great

[@JC-dl1qr]

great video, brief and clear.

[@JoonHeeKim]

Great video. A (very minor) question: isn't it c instead of c^2 when you draw the radius vector of the circle for \beta restriction?

[@Viewfrommassada]

think of it as an equation of a circle with center (0,0)

[@youyangcao3837]

great video, the explanation is really clear!

[@SiDanil]

what the "level curve" means?

[@Thaifunn1]

excellent video! Keep up the great work!

[@garbour456]

great video - thanks

[@zhongshanhu7376]

very good explanation in an easy way!!

[@mnwepple]

Awesome video! Very intuitive and easy to understand. Are you going to make a video using the probit link?

[@jakobforslin6301]

You are awesome!

[@Theateist]

Is the reason to not choose big LAMBDA because we maight get underfitting? If we choose big LAMBDA we get small W and then the output function (hypothesis) won’t reflect our data and we might see underfitting.

[@sasanosia6558]

Amazingly helpful. Thank you.

[@zehuilin8783]

Hey Ritvik, I have a question about this one, I don't really know why we are choosing the point that is far from the origin point. So which direction does the gradient descent and why? Please help me out here, thank you so much!

[@ibrahimkarabayir8963]

nice video , I have a question: lambda depends on c, isnt it?

[@hunarahmad]

thanks for the nice explanation

[@TURBOKNUL666]

great video! thank you very much.

[@sergioperezmelo3090]

Super clear

[@tsrevo1]

Sir, a question about 4:54: I understand that in tax/income example the VARIANCE of the beta0-beta1's is high, since there's an additional beta2 effecting things. However, the MEAN in the population should be the same, even with high variance, isn't it so? Thanks in advance!

[@Hazit90]

excellent video, thanks.

[@lauraarmbrust1639]

Thanks for this really helpful video! Could you explain why the independent variables in A should be standardized for Ridge and Lasso Regression?

[@ronithsinha5702]

Can someone explain why does Ridge regression leads to shrinkage of co-efficients but not entirely zero co-efficients, whereas Lasso causes some co-efficients to become zero entirely.

[@jamiewilliams9271]

Thank you so much!!!!

[@kxdy8yg8]

This is gold indeed!

[@bnouadam]

Impressive

[@ritvikmath]

Thanks !

[@eDogBomb]

What is the intuition behind putting the constraint on the size of the Beta coefficient rather than the standard errors of the Beta coefficient?

[@zhilingpan2486]

Very clear. Thank you!

[@vnpikachu4627]

Amazing!!!

[@myazdani2997]

I love this video, really informative! Thanks a lot

[@abeaumont10]

Great videos thanks for making it

[@1982sadaf]

How can both beta_1 and beta_2 be bounded by the same C? Are the independent variables normalized? (divided by their variance?) otherwise the scale of beta_1 and beta_2 can be drastically different.

[@gueyenono]

In order to normalize data, we divide it by the standard deviation rather than the variance. And that's just the second step after subtracting the mean.

[@LeCoolCroco]

also you can do apply MinMax Scaler... in sklearn for Ridge and Lasso there is "normalize" parameter. normalize : boolean, optional, default False

[@AhmedAbdelrahmanAtbara]

Well, that is the whole point of regularization, isn't it? You don't want these coefficients to produce a polynomial with the exact fit of your data, i.e. over fitting; you want to have a rough fitting which can only happen when you reject any coefficients, and hence solutions, reside outside the bounded domain (the circle). The answer is you don't care how large are these values, no need to normalize them, you just reject any large that which are not subject to the constrain. If the variation is too high and not recommended to ignore then maybe the Ridge Regression is not the right regularization for your data!

[@brendachirata2283]

hey, great video and excellent job

[@danahn5819]

Thank you!

[@lucasrugar6230]

6:30 It should be c, not c^2 on the diagram

[@fireketchupII]

Thank you, this was tripping me up.