8.2. Cross Validation - Python implementation | cross_val_score | Cross Validation in Sklearn

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. This video is about how to implement Cross Validation in Python. For this, we will be using croos_val_score function in sklearn.
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[@NirajBhatt-e8n]

I express my heartfelt gratitude for such clean-concept videos.

[@kandiahchandrakumaran8521]

Excellent video. Well done.
Are you able to create a video to generate nomogram for customer churn?
Thank you.

[@chiragahlawat465]

Sir apart from classification problem, is stratify also useful in regression problem?? Thank you Sir for this beautiful explanation ❤

[@InsPiringMegamind]

Why here we comparing
scores.mean and test score
Why here we use cross validation
And why we calculate scores.mean
Please explain
If scores.mean() not equal to test score
1)!=
2)<
3)>

[@R.Srimathy]

Sir, Thanks for the clear explanation.
I have a question, In 27:30 you are saying that we can use "either of the one (i.e.) cross_validation or train_test_split. Whether you mean we do not need to use train_test_split at all and directly we can use cross-validation to check the model?
If that is the case splitting the dataset is the basic and important step right for machine learning right?
Please correct my understanding

[@satire6344]

I am looking for corss validation and grid search for regression but not found any examples on youtube.. if you can provide that would be excellent. Thanks in advance

[@ayushmangupta6682]

Can we use Cross Validation on Regression problems also

[@mastermatt6090]

very helpful thank you very much

[@mahamadoutogola4001]

You explained very well ❤

[@kaviyapriya6329]

Well explained bro

[@tanmaygupta8288]

good explanation

[@MarioSchwaiger]

How many advertisements are in this video??

[@Q9000-s3s]

Thanks for the tutorial! I'm a beginner in coding, please will you tell me how you would print a report after this step on your video "print(cv_score_lr)" or at any stage if possible? I tried in this way, print(classification_report(y_test, cv_score_lr)), but it's not working?

[@siva_reddy12_13]

Thankyou

[@scokim]

This is a great tutorial showing how to implement test-train-split and cross-val. Also, a neat way to batch compare performances of the models in for-in loop. Very nice !!

[@adriandominiquearante3197]

Thanks for this concise explanation. Is the ppt presentation shareable?

[@anushka.narsima]

I watched a couple vids and each sent a different split into the cross_val_score so I was confused on which one was right, thank you for clearing it up!

[@tosinomotayo2097]

This is a great tutorial. Simple and clear explanation. Thank you for sharing your knowledge.

[@kartikbhange6389]

👍Very nice video.. All confusions are destroyed 😊

[@vikashkumargupta4792]

Thank you for a wonderful explanation

[@arshad1781]

Thanks 👍 for sharing free knowledge 😉

[@user-yr9sf2yr3n]

great! thank you

[@andytiw_]

thanxs man

[@sergioreece6375]

Thank you sir.

[@heyrobined]

I have seen some codes where people use x_train and y_train instead of X and Y.
why is that ? is it a wrong way ?
what will happen if we use X_train and y_train?

[@balajiabhi9039]

here we are using k-fold cross validation which is alternative of train test split. In case of TTS we will have test data and train data which are subsets of actual dataset. In case of TTS we manually split for training and testing purspose but in case of KFCV it internally splits the datasets and finds the accuracy. I suggest you watch earlier video for better understanding @Hey Robin

[@margothabet9938]

U r the best 👍

[@balajiabhi9039]

All these I was afraid of listening these cross validation and hyper parameter tuning concepts.but watching your tutorial on KFCV made me feel very intresting and using this concept\. and entering the Hyper paramter tuning vid. lets hope same kinda content I could get for that too. Anyhow thanks a lot siddhu for providing such a valuable and meaningful content.

[@Siddhardhan]

😇😇

[@SathishKumar-rr1nq]

Super ..

[@elisahorton4833]

I love the speed at which you teach and how thorough you are. Thank you for this!