Support Vector Regression Indepth Maths Intuition In Hindi

Support Vector Regression is a supervised learning algorithm that is used to predict discrete values. Support Vector Regression uses the same principle as the SVMs. The basic idea behind SVR is to find the best fit line. In SVR, the best fit line is the hyperplane that has the maximum number of points
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[@risingrohit7152]

The symbol used here is greek(Z) is ζ(ZETA) , which shows the number of missing points inside the marginal plane, whereas the 'C' shows the points outside the marginal plane

[@ABHISHEKPANDEY-wj3qd]

Hello Sir, In classification tasks, SVM uses the hinge loss function, which penalizes misclassifications and encourages the model to correctly classify data points.

[@vishaladithya9703]

Very neatly explained, Thank you sir.

[@aleenakotadiya]

in classification the marginal plane was drawn as the point closest to the hyperplane . In regression how do you decide where to draw the marginal plane

[@sehrishsangikhan]

teaching method is great sir ,, but ek baat hain ki plzzzz ek small data set ko solve b karay sath in formulas ko use kar k ,,,,bhot mushkil hota hain data set pa ya apply karna ,,,,

[@StoriesFable]

Hello sir, How is it possible the marginal distance of two different classes is same. 3:40s in video. Kindly Explain.

[@arunteja740]

In Live community session, you said eeta(i) as the summation of error data points from point to marginal plane. In this video, you were referring eeta(I) as summation of differences of points from point to hyperplane. Can anyone clarify on this?

[@PrinceVermaIIT-H]

there is two different definition for constant C and zeta. here is define some other def and in classification you said opposite the def. of C . which is correct.(Text here)

[@rasengan4480]

8:59 why did you select that particular point above the Marginal Plane only as the point from which the distance to the Marginal Plane is calculated and labeled as Eta i?

[@emprazon5317]

He just took a random example of outlier , Ig.

[@justsomerandomguy6719]

Thank you for this awesome video. I have a question - What is the i value signifying in the constraint? So what is the i in Ci and the i in ζi? May you please explain.

[@Ash-bc8vw]

i is a subscript notation used to sum over total number of points.

[@justsomerandomguy6719]

@@Ash-bc8vw Thank you for helping out

[@kollaparthianuradha4719]

In short finding hyperplane which can accomodate most of the data points?Is it correct?

[@vrunalkalbande9988]

Easy to understand.....Thanks Sir

[@HarshSingh-zp7jb]

Thankyou so much

[@vasantvyas2512]

Enrolled in fsds sir .... 😇 Every day finding my self better

[@sangitawarade]

How to estimate marginal hyperplanes

[@KeshobMondal-kg7dc]

Can you add one numerical example of SVR.

[@fullthrottlevishal]

eta should be calculated for the points assuming in C hyper parameter am i right sir , like if i take c = 5 , so ill calculate eta for those 5 points , kindly reply

[@likithb3726]

yeah because for other points it will be 0

[@formalindustrialarea5553]

What is y in constraint

[@soumyabrata111]

Mast

[@hummingbird452]

Can you please recommend a book to me .

[@hummingbird452]

the explanation was good . I need a book for machine learnnig

[@saitarun6562]

krish where is the link for SVM Regression in English i think you only added classification videos(2) for SVM please drop regressor link

[@pkumar0212]

👌

[@bhushanbowlekar4539]

its zeta and not eta

[@rasengan4480]

what you're calling Eta is not called Eta

[@Anm.Y_]

Please provide complete info

[@rishabhgoel8936]

bhai jab smjhana nhi aata to video kyon bnate ho