Beautifully explained. Watched the video for visual explanation of Stratified K Fold. You nailed it in your explanation. Thank you.

@MaheshHuddar

6 ай бұрын

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very concise yet detailed, Thanks so much, you just explain what a 2 hours video cant explain in 9 mins. Thanks.

@MaheshHuddar

8 ай бұрын

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I watched the video for a visual demonstration of cross-validation and found it to be exceptionally well explained and easily comprehensible. You did an excellent job in your explanation. Thank you very much.

@MaheshHuddar

3 ай бұрын

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amazing, thanks a lot for clearing the concepts so well.

@MaheshHuddar

8 ай бұрын

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Almost everyone studies from your videos in my college,thank you sir!

@MaheshHuddar

3 ай бұрын

Welcome Do like share and subscribe

@MaheshHuddar

3 ай бұрын

In which college you are studying..?

@yashvardhangoyal9063

3 ай бұрын

@@MaheshHuddar sir SRM

Crisp. To the point. Awesome

@MaheshHuddar

3 ай бұрын

Glad you liked it! Do like share and subscribe

awesome, you did a good job, explaining the topic

@MaheshHuddar

9 ай бұрын

Thank you Do like share and subscribe

Thank you!

@MaheshHuddar

11 ай бұрын

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Great video

@MaheshHuddar

28 күн бұрын

Thank You Do like share and subscribe

How to select the optimum value of K?

Good explanation sir

@MaheshHuddar

8 ай бұрын

Thanks and welcome Do like share and subscribe

Good tutorial

@MaheshHuddar

Жыл бұрын

Thank You Do like share and subscribe

Sir, I could not differentiate between k fold n leave one out, both seemed to be same only

@HarshPatel-iy5qe

3 ай бұрын

lets assume you have 10 samples. In K fold let say we choose k=3 which means we will cerate 3 validation set. training and testing sample in each 3 set will be different. In leave one out is like sliding window technique. as we assume we have total 10 samples so in leave one out , we will create 10 validation set. like 1st have 9 train data 1 test data 2nd have 8 train data 2 test data ....so on

@HarshPatel-iy5qe

3 ай бұрын

lets assume you have 10 samples. In K fold let say we choose k=3 which means we will cerate 3 validation set. training and testing sample in each 3 set will be different. In leave one out is like sliding window technique. as we assume we have total 10 samples so in leave one out , we will create 10 validation set. like 1st have 9 train data 1 test data 2nd have 8 train data 2 test data ....so on

@misahhere9224

3 ай бұрын

thankyou @@HarshPatel-iy5qe

Thank you Sir ❤

@MaheshHuddar

3 ай бұрын

Most welcome Do like share and subscribe

Thanks

@MaheshHuddar

Ай бұрын

Welcome Do like share and subscribe

Wonderful sir

@MaheshHuddar

Жыл бұрын

Thank You Do like share and Subscribe

@maths_impact

Жыл бұрын

@@MaheshHuddar already subscribed

@maths_impact

Жыл бұрын

@@MaheshHuddar sir is stratified k-fold applied in genetic algorithm

What did you mean by the term “example”? Data point/row ?

@MaheshHuddar

8 ай бұрын

Data point

Mrng me exam hai jaldi se padh leta hu ..... Best explanation

@MaheshHuddar

11 ай бұрын

Thank You Do like share and subscribe

@MaheshHuddar

11 ай бұрын

In which university you are studying..?

@057hemantkumar6

11 ай бұрын

@@MaheshHuddar RGPV

@BheezHandle

8 ай бұрын

hahaha, deadliner, hope you made the best out of the examination already.....

do we train same model in all fold?

@MaheshHuddar

8 ай бұрын

Yes

Tqsm sir i want notes can u pls send me😊

@MaheshHuddar

7 ай бұрын

Thank You