Ridge Regression

[@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.

[@tzu-chunchen5139]

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

[@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

[@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!

[@taareshtaneja7523]

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

[@BhuvaneshSrivastava]

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

[@ritvikmath]

I appreciate it!

[@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).

[@bettychiu7375]

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

[@q0x]

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

[@alecvan7143]

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

[@TahaMVP]

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

[@murraystaff568]

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

[@SarahPourmolamohammadi]

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

[@yxs8495]

This really is gold, amazing!

[@nikunjgattani999]

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

[@Lisa-bp3ec]

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

[@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 :)

[@surajshivakumar5124]

This is literally the best video on ridge regression

[@akino.3192]

You, Ritvik, are simply amazing. Thank you!

[@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.

[@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

[@abhichels1]

This is gold. Thank you so much!

[@cu7695]

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

[@theoharischaritidis4173]

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

[@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.

[@Viewfrommassada]

I'm impressed by your explanation. Great job

[@ritvikmath]

Thanks! That means a lot

[@ethanxia1288]

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

[@nickb6811]

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

[@charlesity]

Stunning! Absolute gold!

[@wi8shad0w]

seriously!!!

[@mortezaabdipour5584]

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

[@Krishna-me8ly]

Very good explanation in an easy way!

[@yanlinwang5703]

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

[@jhhh0619]

Your explanation is extremely good!

[@mohamedgaal5340]

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

[@ritvikmath]

No problem!

[@OmerBoehm]

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

[@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.

[@vishnu2avv]

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

[@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😶‍🌫

[@e555t66]

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

[@sanketchavan8]

best explanation on ridge reg. so far

[@abhijeetsingh5049]

Stunning!! Need more access to your coursework

[@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!

[@wi8shad0w]

THIS IS ONE HELL OF A VIDEO !!!!

[@intom1639]

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

[@SUBHRASANKHADEY]

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

[@RLDacademyGATEeceAndAdvanced]

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.

[@llmstr]

in your drawing at 9:00, if the level curves is reverted back to 3D graph, is the axis that going up the loss function? just want to clarify. thanks!

[@Thaifunn1]

excellent video! Keep up the great work!

[@nicolasmanelli7393]

I think it's the best video ever made

[@HeduAI]

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

[@Sytch]

Finally, someone who talks quickly.

[@prabhuthomas8770]

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

[@LossAndWaste]

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

[@youyangcao3837]

great video, the explanation is really clear!

[@JC-dl1qr]

great video, brief and clear.

[@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.

[@sagarsitap3540]

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

[@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!

[@shiva6016]

simple and effective video, thank you!

[@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.

[@kamesh7818]

Excellent explanation, thanks!

[@adityakothari193]

Excellent explanation .

[@sasanosia6558]

Amazingly helpful. Thank you.

[@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?

[@kartikkamboj295]

Dude ! Hats off 🙏🏻

[@tamoghnamaitra9901]

Beautiful explanation

[@myazdani2997]

I love this video, really informative! Thanks a lot

[@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.

[@faeritaaf]

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

[@vinceb8041]

Can anyone help me understanding the effects of multicollinearity? I understand that the estimators will be highly variable, but why would they be very large?

[@benxneo]

thats actually an interesting question, have you found an explanation to this? I seem to only be able to say that regression depends on variables to be independent on each other, and multicolinearity makes it sensitive to small changes. But why is it that coefficients are larger I cant seem to understand.

[@adarshnamdev5834]

@ritvik when you said that the estimated coefficients has small variance does that implies the tendency of obtaining different estimate values of those coefficients ? I tend to confuse this term 'variance ' with the statistic Variance (spread of the data!).

[@benxneo]

Variance is the change in prediction accuracy of ML model between training data and test data. Simply what it means is that if a ML model is predicting with an accuracy of "x" on training data and its prediction accuracy on test data is "y" Variance = x - y A smaller variance would thus mean the model is fitting less noise on the training data, reducing overfitting. this definition was taken from: datascience.stackexchange.com/questions/37345/what-is-the-meaning-of-term-variance-in-machine-learning-model Hope this helps.

[@adarshnamdev5834]

@@benxneo thanks mate!

[@zw7453]

best explanation ever!

[@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.

[@mnwepple]

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

[@abeaumont10]

Great videos thanks for making it

[@meysamsojoudi3947]

It is a brilliant video. Great

[@brendachirata2283]

hey, great video and excellent job

[@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?

[@Hazit90]

excellent video, thanks.

[@TURBOKNUL666]

great video! thank you very much.

[@kxdy8yg8]

This is gold indeed!

[@msloryg]

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

[@dimar4150]

Can someone explain what the level curves mean?

[@carlitors]

Google contour plots, not something that can be easily explained on a video. Usually taught in first few lessons of Calculus III.

[@zhongshanhu7376]

very good explanation in an easy way!!

[@hunarahmad]

thanks for the nice explanation

[@SiDanil]

what the "level curve" means?

[@xiaoguangzhao34]

awesome video, thank you very much!

[@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!

[@dorukhansergin9831]

Thank You, Great Video! Just a possbile correction, at 6:35 shouldn't the radius be c instead of c^2?

[@volintine]

Dorukhan Sergin probably late, but no equation of circle is x^2 + y^2 = r^2

[@kevinwong8020]

I was taught that the name Ridge Regression comes from the lambda I matrix. It looks like a ridged staircase shape.

[@ibrahimkarabayir8963]

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

[@JuPeggy]

excellent video! thank you!

[@jakobforslin6301]

You are awesome!

[@ibrahimkarabayir8963]

and , is c a value that minimizes VIF value?

[@justinm1307]

this is great stuff

[@nickwagner5173]

also, why can't we solve for lamda and the betas by taking partial derivatives and setting each equation to zero and then solve the system of equations?