Ridge Regression for Beginners!

  Рет қаралды 68,394

Prof. Ryan Ahmed

Prof. Ryan Ahmed

Күн бұрын

Пікірлер: 68
@agila.p9807
@agila.p9807 2 жыл бұрын
You have the skill to simplify a complex topic which can be understood by everyone. Please continue your great work. This world needs more teachers like you.
@Kmysiak1
@Kmysiak1 5 жыл бұрын
I've watched dozens of videos on regularization and your explanation is perfect! thanks!
@jgianan
@jgianan Жыл бұрын
Wow! It took me several rewinds to understand that from my professor and I got it in 3 mins with the way you explained and visualized it! Thank you!
@shroukezz8878
@shroukezz8878 Жыл бұрын
the best explanation for the ridge regression I have ever listen
@mthandenimathabelacap5466
@mthandenimathabelacap5466 Ай бұрын
Clear explainantion of the Ridge() model. Very intuitive. SUBSCRIBED.
@HL-dw4dl
@HL-dw4dl 3 жыл бұрын
Great video for people like me who are beginners and don't want to go deep in the Statistics part of it but a simple explanation for data science. 🧡 from India.
@goodwavedata
@goodwavedata 4 жыл бұрын
I loved this video. I've heard about "reducing the coefficient values" in so many other places, but you explained the 'why' behind this better than any of the others that I saw.
@ksh2106
@ksh2106 Жыл бұрын
Thank you for explaining bias and variance and not just moving forward without the explanation!!
@MarcoBova
@MarcoBova 6 ай бұрын
Really a pristine work, in explaining the ideas behind the concept. I found it really useful for having an overview look before dealing with all the math behind. Thanks
@winniesebapu1364
@winniesebapu1364 2 жыл бұрын
You explained it in simple way and with a short video. very effective
@geneticengineer7720
@geneticengineer7720 4 жыл бұрын
You made it easy to understand. But where do you get the alpha and slope? From the testing data set? Then the testing data set becomes the training data set.
@spider279
@spider279 2 жыл бұрын
Wow , your explaination are too good, it's my first time seeing your video and i'm really satisfied
@Unbiased27
@Unbiased27 4 жыл бұрын
The best explanation I've heard on ridge regression. Straightforward and precise! Thank you very much!
@Reglaized
@Reglaized 2 жыл бұрын
Great explanation! Thank you!
@2904sparrow
@2904sparrow 4 жыл бұрын
Very well explained, finally i got it! Many thanks.
@gzuzchuy505
@gzuzchuy505 4 жыл бұрын
Perfect explanation!! You explained it in simple way and with a short video. Thanks, keep the good work
@ThePiratefan96
@ThePiratefan96 11 ай бұрын
Very helpful! Thank you Professor!
@jeanyeager4252
@jeanyeager4252 Жыл бұрын
Thank you for the quick and easy to understand tutorial
@professor-ryanahmed
@professor-ryanahmed Жыл бұрын
Glad it was helpful!
@zhannadruzhinina4235
@zhannadruzhinina4235 Жыл бұрын
This is a great video, thank you!
@sues4370
@sues4370 Жыл бұрын
Thank you! This is a very helpful explanation and visualization of ridge regression.
@professor-ryanahmed
@professor-ryanahmed Жыл бұрын
You're very welcome!
@fergavilan132
@fergavilan132 2 жыл бұрын
The only and first video that allowed me to understand this shit. Thanks!!
@SajidHussain-dt7ci
@SajidHussain-dt7ci 2 жыл бұрын
really appreciate your effort thanks for help!
@shivu.sonwane4429
@shivu.sonwane4429 3 жыл бұрын
In ridge regression alpha never be 0 . ☺️ Easy and clear explanation
@wingyanwong3208
@wingyanwong3208 3 жыл бұрын
Very good explanation. Thank you. It gives me the idea of ridge regression.
@FaisalR-n4z
@FaisalR-n4z 11 ай бұрын
Amazing explanation, thanks ryan
@professor-ryanahmed
@professor-ryanahmed 7 ай бұрын
My pleasure!
@palaknath
@palaknath 4 жыл бұрын
Thank you Sir! the great explanation made the concept seem so easy!
@quant-prep2843
@quant-prep2843 3 жыл бұрын
what if model needs high sensitivity to dependent variable ?
@ThuyTran-bw7dq
@ThuyTran-bw7dq 2 жыл бұрын
Thank you sir, it's so simple!
@kislaykrishna8918
@kislaykrishna8918 3 жыл бұрын
great crystal clear
@khaledsherif7056
@khaledsherif7056 4 жыл бұрын
I like how you explained that well in a 7 min video.
@Luckys1191
@Luckys1191 2 жыл бұрын
Good Explanation....
@cdhaifule
@cdhaifule 3 жыл бұрын
Wonderful explanation. Thank you.
@faizanzahid490
@faizanzahid490 4 жыл бұрын
Really appreciate the tutorial, just one query, Does regularisation always reduce the slope? I mean i think it's possible for the test dataset to have more slope than training set.
@marcelocoip7275
@marcelocoip7275 2 жыл бұрын
Black hole here... Looking for this answer...
@KrishnenduJ-hc5fg
@KrishnenduJ-hc5fg Жыл бұрын
Regularisation minimises the sum of squared errors while also minimising the sum of squared magnitudes of the coefficients. This pushes the ridge coefficients closer to zero. But yes, if the penalty term is too small, the slope may resemble that of OLS.
@KrishnenduJ-hc5fg
@KrishnenduJ-hc5fg Жыл бұрын
So it is highly unlikely for regularisation to increase the slope than that of OLS.
@ekleanthony7997
@ekleanthony7997 3 жыл бұрын
Awesome Explanation. thanks!
@professor-ryanahmed
@professor-ryanahmed 3 жыл бұрын
Glad you enjoyed it! thanks!
@benuploads7964
@benuploads7964 2 жыл бұрын
amazing explanation!
@yl3046
@yl3046 5 жыл бұрын
Good Intuition. Contradicting in the slides whether ridge regression increase/decrease for bias and variance.
@tyman1449
@tyman1449 5 жыл бұрын
Thank you for your short video. But I did not understand why we should minimize the slope. It is just a possibility and depends on test data. You may increase the slope to get minimum residuals.
@SumitKumar-uq3dg
@SumitKumar-uq3dg 4 жыл бұрын
Minimizing or maximizing is decided after looking at the total errors. If maximizing increases the error then we will go to minimizing the slope.
@cesar3550
@cesar3550 4 жыл бұрын
Great video and great english as well, you gained a new sub
@Cherdanye
@Cherdanye 3 жыл бұрын
5:01 door opens
@malinyamato
@malinyamato Жыл бұрын
great intro !
@professor-ryanahmed
@professor-ryanahmed Жыл бұрын
I'm glad you like it
@chintunannepaga579
@chintunannepaga579 3 жыл бұрын
excellent concept explanation.. thank you
@Muziekmixen
@Muziekmixen 4 жыл бұрын
Suggestion: You explained very well Ridge & Lasso Regression, make also one for Elastic Net!
@ramspalla8036
@ramspalla8036 2 жыл бұрын
Hi Ryan, Can you please do a video on Elastic Net Regression?
@talkingabout-h8d
@talkingabout-h8d 4 жыл бұрын
Thank you for this *great explanation*
@NoelGeorge-l4g
@NoelGeorge-l4g Жыл бұрын
How does increasing Lambda trem reduces the slope. We are multiplying Lambda with Slope right, which is constant?
@Pankaj_Khanal_Joshi
@Pankaj_Khanal_Joshi 11 ай бұрын
Sir how do we know that during regularization we have to increase or decrease the slope.
@leiyarabe7482
@leiyarabe7482 4 жыл бұрын
👏👏👏👏👏👏 well explained!
@spandangude8973
@spandangude8973 3 жыл бұрын
thank you for the video. do you speak Farsi ?
@raedos1
@raedos1 3 жыл бұрын
It just feels like a fancy way to include your testing set into your training set, essentially making 100% of your data a trainingset. What is the difference between those?
@VBeniwal_IITKGP
@VBeniwal_IITKGP 4 жыл бұрын
Thank you sir🙏🙏
@yugoeugis6733
@yugoeugis6733 4 жыл бұрын
Education is about pedagogy. Who teaches. Here's a good one.
@marcelocoip7275
@marcelocoip7275 2 жыл бұрын
But how ridge works if the variance decrease with a steeper slope?
@adrianaayluardo8583
@adrianaayluardo8583 5 жыл бұрын
Thank you!
@Frdy12345
@Frdy12345 Жыл бұрын
Isn’t alpha actually lambda?
@Nimkrox
@Nimkrox 5 жыл бұрын
The explaination is good, but I think that your example could be better. Having 3 points in the training set and 5 points in testing set is not a good practise. Also your 3 training points will give the same line every time, so again: not the best example
@deathwiddle3826
@deathwiddle3826 11 ай бұрын
your such a hater😢
@a7med7x7
@a7med7x7 6 ай бұрын
The example is perfect, it is for illustration, and textbooks use the same amount for training data points, it’s better to emphasize the idea of more testing data points to show the mainstream and pattern of the data, in reality, the dataset you use will never be as much as the samples it was testing or seen on. The 3 similar training data points are the same reason why the problem occurs, and the ideal mechanism for solving it is to deviate your model from it.
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