Hi sir, thanks a lot for such valuable videos and crisp information. Can you please tell me why exactly a high coefficient value is a problem in regression models? Also is very low coefficient values also a problem? Thanks in advance.
U told that l1 and l2 is only available for regression. But I have seen them for feature selection for textual dataset(although in textual data features are transformed into vector form and have numerical values) . So pls clarify the things that whether they used for feature selection also?
Hi aman can I request you to make a video on what's the best approach of dealing with complex data in real world . as we know in real time the data is very unstructured and most of the time data doesn't exist in CSV form. But unfortunately many of the learning available on RUclips is in perspective of analyzing data which is in CSV form . Can you please enlighten these points in your upcoming videos including the best and practical approach . For example how to work with JSON data in data science project ,, how to work with XML files etc? Regards Sanyam
what do you mean by L1 and L2 regularization works only with Linear regression, decision tree based algo does have other way of regularization? You mean to say L1 and L2 are not in tree based algo? L1 and L2 are also used in decision tree based algo for example catboost regression has L2 (l2_leaf_reg) regularization technique
Thank you for the explanation. Would have been useful to see how this would work in practice using an example in Excel using a small dataset or in Stata.
Sir since we already have learning rate to arrive at the optimum coefficients then why do we need to use Regularization ? Aren't both of them serving the same purpose?
Linear Regression => (XTX)-1XTY Ridge Regression: (XTX+PI)-1XTY where P is penalty, I is Identity Matrix Lasso Regression=>please mention Elastic Net =>Please Mention
Hi Aman, BIG FAN OF YOUR WORK!! I noticed you give DS training and filled the google form right away! Sadly didn't receive any email. Can you help me with my issue? Should i receive an email? I'm super interested. Thank you!
Hi ,not getting enough bandwidth for training now, however I am working on a course, will share the update soon, many thanks for watching videos and staying connecetd.
My lasso regression is getting wrong results. It is giving all coefficients as zero except the constant and R2 score as --0.001825328970232576. Someone please help.
It's not about staying in India or anywhere else. Unfortunately we need to speak in English in office and interviews and this channel is completely in English so that everyone can understand.
After my data science classes I used to watch the concepts through your videos and it helped me a lot in understanding... 😃😃
Thanks Mrutyunjaya.
Every one can understand ur explanation.....neat and clear 👍
Thanks a lot Brahmadanna.
Beautiful explanation sir, I regret of not watching this video before my interview but anyhow I am glad I got to know it now.
Thanks a lot Shanmukh 😊
Thanks ! all doubts cleared ..!
The word sweet spot can actually impress the interviewer I guess :)
feels like getting a lecture from one of my friends at last night before the exam
loved the way u teach and your voice is amazaing. I wish for the growth of this channel
Hi sir, thanks a lot for such valuable videos and crisp information.
Can you please tell me why exactly a high coefficient value is a problem in regression models? Also is very low coefficient values also a problem?
Thanks in advance.
Excellent teacher. Thank you sir for such a wonderful explanation. :)
Welcome :)
Amazing, your teaching skills are really awesome sir! Thanks for this great work
Welcome Sudhanshu.
Really Learned a lot Sir..your teaching skills are amazing..Super..
Thanks a lot Kirandeep.
Very nicely explained 💯💯
Please explain the maths behind feature selection using lasso and not ridge.
Yes Navodit, next video me wahi aaega.
Thank you for informative video, how is accuracy less is in overfitting scenario?
U told that l1 and l2 is only available for regression. But I have seen them for feature selection for textual dataset(although in textual data features are transformed into vector form and have numerical values) . So pls clarify the things that whether they used for feature selection also?
good explanation with keeping the audience understanding in me
one of the good explanations i have seen for this topic, good work
Thanks Gaurav.
Sweet Spot ❌ Technical word - Balanced Fit
Very clear explanation 👍👍👍
Thanks Christy.
Which is the best/better regularization technique , and which is used for variable selection
Amazing explaination Sir !!!
Thanks vaiya😊
Very Well explained.
Wouldn't cubing the slope(instead of squaring) in the ridge regression penalty decrease the loss function even more? If yes, why don't we do that?
Squaring a function makes it differentiable. Hence.
Hi aman can I request you to make a video on what's the best approach of dealing with complex data in real world . as we know in real time the data is very unstructured and most of the time data doesn't exist in CSV form. But unfortunately many of the learning available on RUclips is in perspective of analyzing data which is in CSV form . Can you please enlighten these points in your upcoming videos including the best and practical approach . For example how to work with JSON data in data science project ,, how to work with XML files etc?
Regards
Sanyam
i wish this channel reached 100K very soon
Thank you so much. If you guys keep liking and sharing, anything is possible. Your feedback is highly appreciated!
1:09 that laugh 🤣🤣
I understand the struggle.
THANK YOU MR. AMAN SIR
Thank you sir .
You just made tuff topics so easy.🙏
Thanks Inderjeet.
great explanation sir
Awsome description
what do you mean by L1 and L2 regularization works only with Linear regression, decision tree based algo does have other way of regularization? You mean to say L1 and L2 are not in tree based algo? L1 and L2 are also used in decision tree based algo for example catboost regression has L2 (l2_leaf_reg) regularization technique
finished watching
Amazing Teaching Sir.. Thank You....
Welcome Adarsh
Thank you for the explanation. Would have been useful to see how this would work in practice using an example in Excel using a small dataset or in Stata.
Glad it was helpful!
Excellent explanation 👍🏻
Thanks Sourav.
Great... Helped a lot..
Thanks Shubhajit.
Sir since we already have learning rate to arrive at the optimum coefficients then why do we need to use Regularization ? Aren't both of them serving the same purpose?
fnished watching
sir can we use Lasso and ridge for feature selection in multi-class classification? say for IRIS data? or it is only for binary problem? please reply
Mulyi class also you can
Awesome sir
Sir, can you please make computer vision and CNN videos?
Excellent explanation. Subscribed!
Thanks Keemster
sir, can you make more videos on deep learningg
Bro please include a exercise that uses all these.
Thanks Aaron for watching. Will do.
Great...
nice explanation
Thanks and welcome
bro how do you find the equation (bo+b1) after find the fist cost function is high
Is it possible to use ridge regression to impute univariate time series? Thanks
we can do.
Linear Regression => (XTX)-1XTY
Ridge Regression: (XTX+PI)-1XTY where P is penalty, I is Identity Matrix
Lasso Regression=>please mention
Elastic Net =>Please Mention
I am not sure but we use l2 in neural networks i saw Andrew Ng lecture.
If my slope is coming very less but I want my model slope to be more what's thought in that
Slope will come based on data, why u want to change it?
Hi Aman, BIG FAN OF YOUR WORK!! I noticed you give DS training and filled the google form right away! Sadly didn't receive any email. Can you help me with my issue? Should i receive an email? I'm super interested.
Thank you!
Hi ,not getting enough bandwidth for training now, however I am working on a course, will share the update soon, many thanks for watching videos and staying connecetd.
@@UnfoldDataScience Thanks for the quick response! Already turned on the notification bell!
My lasso regression is getting wrong results. It is giving all coefficients as zero except the constant and R2 score as --0.001825328970232576. Someone please help.
samjh nhi aaya bhai.where to use l1 and where to use l2? try explaining in dnn model
Exact what i want AMAN
Thanks Vishal.
Why L1 regularization creates sparsity ???
Hi Mukesh, does it?
@@UnfoldDataScience yes Aman it creates sparsity
not understood
Feedback taken, thanks 🙂
Bhai Hindustan me rhete ho toh hindi me bhi samjho na
It's not about staying in India or anywhere else.
Unfortunately we need to speak in English in office and interviews and this channel is completely in English so that everyone can understand.