i did not find proper online stuff on Data science till you come . Now you came i am amazed at watching and learning data science stuff from you. You are my real Guru ,awesome Sir.
Hrithik Roshan who? You're the real Krish! Thanks so much man, saved my ass as I'm reviewing and revising for finals now. God bless and you've earned yourself a subscriber.
Simple and lucid English with examples makes it easy to understand.. don't worry about comments, be cool as you seems to be and do your job of giving knowledge..
Great job, finally I understand what is the purpose of cross validation. I appreciate especially the part when you explained how to communicate cross validation to the stake holders. Thanks again
superb sir, hats off for your simplified explanation with comparison too. very helpful sir, looking forward for videos on other validation methods, thank you once again
I've been watching your videos on ML. These are simply brilliant. Your explanation is awesome. Could you please provide cards at the right top when you are referring to your other videos. it helps us more. Thanks for your efforts brother...
Hi Krish..your videos are awesome..the kind of passion and enthusiasm you have for data science and also for empowering others with same knowledge is exemplary. I am trying to learn data science/ML to employ them in Industrial Systems. Pls help me what approach should i follow. Share sources where i can learn to employ them in Industrial systems.
Good Explanation however I have one doubt. The model is trained k times with different datasets so, the hyperparameters of the each model will be different. How to determine and see the best model and its associated hyperparameter. Thank You
Hello sir, You inspire me a lot in the data science and ML journey. Plz can i kindly correct something that I have noted in the vid. I believe we cannot calculate the number of K as you have done it bcoz we mostly just choose a suitable number. If am wrong plz bring me back on track. I believe what you were trying to check is the portion size of each partition in a fold, that can be calculated using the size of the train_test split which i bliv in the example you used is 80%20% therefore using the 1000 records ->1000 of 20% =200 in each partition of a fold then the K we choose it to our preference. Plz bring me back on track if am wrong.
Hi Krish, nice explanation on CV. But can you show in the codes where i find this accuracy for any kind of CV method. Because I'm a little confused about that part. Or please suggest the video if you have already done that
the cross validation is applied in the Train data (ex: 80 :20, total observations: 1000, train =800, test=200 , if k=4 then it is 200 each fold, so one of the fold is considered as Cross validation , but each time it will change the FOLD-1,FOLD-2,FOLD-3,FOLD-4 . total 4 times it will calculate the accuracy score then average it and give the final score.
Hi, thank you for the video. I was thinking that the fourth method is very close to moving averages. Could we also have done a 5 day moving average for prediction or as an alternate?
Hi Krish, the video is very intuitive and clearly explained! Just wanted to know, do we need to perform cross-validation during hyperparamter tunning? Or should we do it two times, that is (i) duing hyper-paramter tuning and (ii) then after training the model with optimized paramters? In K-fold cross-validation, we have k models. Which one should we deploy? Thanks in Advanced!
Awsome tutorial,Its been a while since i started to follow your videos and the results are pretty much good and easy to understand from base.I have a query regarding this video,At 14:00 what do you mean by both class ? and what do you mean when you said instances of classes thank you!
so at 14:00 sir was explaining cross validation with example of binary classification that means we have two class so our output will be either 1 or 0....this is what he meant by two class and he meant the same when he said instances of class.... hope u get my point.
i did not find proper online stuff on Data science till you come .
Now you came i am amazed at watching and learning data science stuff from you.
You are my real Guru ,awesome Sir.
Hrithik Roshan who? You're the real Krish! Thanks so much man, saved my ass as I'm reviewing and revising for finals now. God bless and you've earned yourself a subscriber.
just when I started thinking engineering is so difficult, I found you. thanks for such good explaination
this is very close to my university course here in Canada, in fact, you have approached it in a simpler way, which is great
easily the best data science channel out there!
thank you
So Detailed explanation as always
Krish you're amazing, to be honest, I have learnt many things regarding data science from your channel keep going, you are doing awesome.
thanks it really removes my headache of understanding that split stuff of k-fold
First time watching your tutorial
Quality content, clear and crisp
Subscribed👍
Keep making more!!
Krish Naik, you're a G!!!! Thanks for taking your valuable time to make these videos for us!
You are Exceptionally good, the clarity about the concept is on point ! Dam
Crisp and Clear. thank you Krish.
Thank you so much Krish Sir, As usual Great Explanation😊
Simple and lucid English with examples makes it easy to understand.. don't worry about comments, be cool as you seems to be and do your job of giving knowledge..
Great job, finally I understand what is the purpose of cross validation. I appreciate especially the part when you explained how to communicate cross validation to the stake holders. Thanks again
I have my deep learning exam in few days ur lectures are really helpful for revision
Krish! This video simplifies the concept cross validation brilliantly. Good stuff Homie!
Best explanation got cleared at first instance
Sessional tomorrow. Couldn't be explained better. Thank you, sir.
Superb!!!!!!!!!! . Simply Excellent And Very Well Explained.
Thank you Krish. Have been watching your videos for my uni project. Really helpful contents and clear explanations!
Binge watching your awesome machine learning playlist!
Best Explaination ever!! Thank you Sir
Excellent videos sir.. thanks alot
superb sir, hats off for your simplified explanation with comparison too. very helpful sir, looking forward for videos on other validation methods, thank you once again
Great videos Krish thank you so much for sharing
Very nice presentation and very much informative.
This video was very helpful. Thank You!
You are the best. Thank you.
Thank you so much.. God bless you too !
Krish you r awsome teacher
Crystal clear explanation !!!
Much respect to you, Sir.. for sharing this detailed knowledge for free. May God Bless you always
700k subscribers with a board, sir, you are a legend! ❤
very well explained, thank you very much!
Thanks Krish .Great Explanation
You explained it SO well, thank you so much!
I've been watching your videos on ML. These are simply brilliant. Your explanation is awesome.
Could you please provide cards at the right top when you are referring to your other videos. it helps us more.
Thanks for your efforts brother...
Good job Mr. Krishna
How comes i didnt subscribe for krish,,Oh man,You got my respect.Thumps up to you
Great video.
Thank you so much for this nice explanation.
This video is amazing thanx
Thank you very much Krish
Amazing Thanks a lot Sir
subscribed! keep up the good work
Thank you soo much sir for this video clears my concepts
You are doing great service to students, hats off to you. Can you please mention the order of DS project. Thank you
Thanks a ton. You will reap good.
Thank you so much for a clear explanation of this concept
very well explained.. thank you
very nice informative videos
Excellent video.
Awesome Explanation Man.
Thank you
Nice explanation. Thank you so much. Good luck
God Bless You Too.. Thanks!
Amazing explaination..........Thanks
gr8 video
Thank you so much sir.
Thank youuu !!!! ❤
excellent explanation could u explain genialized linear modal pls👍
Hi Krish..your videos are awesome..the kind of passion and enthusiasm you have for data science and also for empowering others with same knowledge is exemplary. I am trying to learn data science/ML to employ them in Industrial Systems. Pls help me what approach should i follow. Share sources where i can learn to employ them in Industrial systems.
no github link?
please do make a video on different types of encoding teachniqe
Great job Sir, Thank you very much ♣
Thanks a lot krish. Do you have a community or a telegram channel
Please teach multivariate timeseries data for classification and clustering
Thankyou so much
Thank you.
detailed explantion about CV
Awesome
Hi Krish,one data means one datapoint or one row
Brilliant..!
Sir your videos are awesome, can you also do a video on dummification and prediction window size?
Really nice video..How to decide which CV to apply ( K told or stratified ) ?
Good Explanation however I have one doubt. The model is trained k times with different datasets so, the hyperparameters of the each model will be different. How to determine and see the best model and its associated hyperparameter. Thank You
Sir your like a God to Data science learners so requesting to please put all videos in a order in playlist :)
I am not a God...Nobody can be God
thanks a lot for ur video. Can u kindly tell how to find accuracy of a CNN model using k fold cross validation.
Ok
Hello sir, You inspire me a lot in the data science and ML journey. Plz can i kindly correct something that I have noted in the vid. I believe we cannot calculate the number of K as you have done it bcoz we mostly just choose a suitable number. If am wrong plz bring me back on track. I believe what you were trying to check is the portion size of each partition in a fold, that can be calculated using the size of the train_test split which i bliv in the example you used is 80%20% therefore using the 1000 records ->1000 of 20% =200 in each partition of a fold then the K we choose it to our preference. Plz bring me back on track if am wrong.
Have a little confusion is the test and validation set are the same or different
Hi Krish, nice explanation on CV. But can you show in the codes where i find this accuracy for any kind of CV method. Because I'm a little confused about that part. Or please suggest the video if you have already done that
So like for every dataset.. Cross validation is mustt???
Thankyou
When we apply hyper parameters tuning do it apply on k fold or stratified cv
Will it be a good idea to suffle the data and then perform k fold CV?
Hi Krishna, nice video. Could you please let me know cross validation is applied on train data or total data?
the cross validation is applied in the Train data (ex: 80 :20, total observations: 1000, train =800, test=200 , if k=4 then it is 200 each fold, so one of the fold is considered as Cross validation , but each time it will change the FOLD-1,FOLD-2,FOLD-3,FOLD-4 . total 4 times it will calculate the accuracy score then average it and give the final score.
Thanks
Sir, the purpose of cross-validation is to reduce the variance, not bias, correct?
Can some share the link of the project where he has used timeseries cross validation
Hi, thank you for the video. I was thinking that the fourth method is very close to moving averages. Could we also have done a 5 day moving average for prediction or as an alternate?
Hi Krish, the video is very intuitive and clearly explained! Just wanted to know, do we need to perform cross-validation during hyperparamter tunning? Or should we do it two times, that is (i) duing hyper-paramter tuning and (ii) then after training the model with optimized paramters? In K-fold cross-validation, we have k models. Which one should we deploy? Thanks in Advanced!
you can use Nested cross validation and do both at the same time
Hello, do you have code implementation for Time series cross validation? Thank you so much.
super sir
In leave one out what is the problem if it has low bias. Ideally algo should have low bias low variance.
computational power...it will take a lot of time to process completely....
thANKS SIR
can you suggest to me the book for cross-validation and k fold validation? which contains with example with solutions?
Awsome tutorial,Its been a while since i started to follow your videos and the results are pretty much good and easy to understand from base.I have a query regarding this video,At 14:00 what do you mean by both class ? and what do you mean when you said instances of classes thank you!
so at 14:00 sir was explaining cross validation with example of binary classification that means we have two class so our output will be either 1 or 0....this is what he meant by two class and he meant the same when he said instances of class.... hope u get my point.
hello sir!! Can you please upload the practical example on above mentioned different types of cross-validation...specially on stratified cv
I am including some a fraction of my training data for validation. Is it a acceptable thing to do?
Is it test set or validation set, i am confused
... 😕🤔🤔🤔
Teaching quality is good but voice is not audible plz use 🎙️