K-Fold Cross Validation, Stratified K-Fold, Leave-one-out Leave-P-Out Cross Validation Mahesh Huddar

K-Fold Cross Validation, Stratified K-Fold Cross Validation, Leave-one-out Cross Validation, and Leave-P-Out Cross-Validation in Machine Learning by Mahesh Huddar
The following concepts are discussed:
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K-Fold Cross Validation in Machine Learning,
Stratified K-Fold Cross Validation in Machine Learning,
Leave-one-out Cross-Validation in Machine Learning,
Leave-P-Out Cross-Validation in Machine Learning
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[@lfjv]

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

[@MaheshHuddar]

Welcome
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[@shiwanideshmukh148]

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]

Welcome
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[@BheezHandle]

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

[@MaheshHuddar]

Welcome
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[@yashvardhangoyal9063]

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

[@MaheshHuddar]

Welcome
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[@MaheshHuddar]

In which college you are studying..?

[@yashvardhangoyal9063]

@@MaheshHuddar sir SRM

[@khanatohhainabhai7541]

Very informative video sir☺️

[@debajyotimukhopadhyay]

Crisp. To the point. Awesome

[@MaheshHuddar]

Glad you liked it!
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[@priyam86f]

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

[@MaheshHuddar]

Welcome
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[@057hemantkumar6]

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

[@MaheshHuddar]

Thank You
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[@MaheshHuddar]

In which university you are studying..?

[@057hemantkumar6]

@@MaheshHuddar RGPV

[@BheezHandle]

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

[@misahhere9224]

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

[@HarshPatel-iy5qe]

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]

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]

thankyou @@HarshPatel-iy5qe

[@shaikhuzma786]

Tqsm sir i want notes can u pls send me😊

[@MaheshHuddar]

Thank You

[@miguel5zx]

awesome, you did a good job, explaining the topic

[@MaheshHuddar]

Thank you
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[@sathirawijeratne7872]

Thankyou🎉

[@MrKhan-gb3rc]

How to select the optimum value of K?

[@sahilsharma2867]

Good explanation sir

[@MaheshHuddar]

Thanks and welcome
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[@Vinit_Gambhir]

Thank you Sir ❤

[@MaheshHuddar]

Most welcome
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[@shahulrahman2516]

Great video

[@MaheshHuddar]

Thank You
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[@SazzadHissain]

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

[@MaheshHuddar]

Data point

[@tecnom7133]

Thanks

[@MaheshHuddar]

Welcome
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[@FaruqAtilola]

Thank you!

[@MaheshHuddar]

Welcome
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[@messiisthebest]

do we train same model in all fold?

[@MaheshHuddar]

Yes

[@mouradchaa5514]

Good tutorial

[@MaheshHuddar]

Thank You
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[@maths_impact]

Wonderful sir

[@MaheshHuddar]

Thank You
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[@maths_impact]

@@MaheshHuddar already subscribed

[@maths_impact]

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

[@mr.gouravkale]

Where is python code sir?

[@everythinginworld3205]

sir plz give me link to ppt