All Type Of Cross Validation With Python All In 1 Video

Building machine learning models is an important element of predictive modeling. However, without proper model validation, the confidence that the trained model will generalize well on the unseen data can never be high. Model validation helps in ensuring that the model performs well on new data, and helps in selecting the best model, the parameters, and the accuracy metrics.

In this guide, we will learn the basics and implementation of several model validation techniques, mentioned below:
#crossvalidation
Hold Out Validation
K-fold Cross-Validation.
Stratified K-fold Cross-Validation
Leave One Out Cross-Validation.
Repeated Random Test-Train Splits
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[krishnaik]

Dont forget to subscribe my vlogging channel to see motivation and Data Science Q&A videos.

[rog]

We need more *All in 1 video* tutorials !! Great work as always !!

[rhevathivijay]

Even in college days.. I dint go to class before 10 ..minutes.. But am here waiting even before 15 minutes... Hats off to you krish sir and sudhanshu sir... for DS and ML aspirants..

[sachinpathania]

Thanks Krish for this video, i was looking for such content from long time, i had many doubts in K fold and LOOCV, finally all doubts got cleared.

[satyamtripathi]

krish naik sir plss make a video on light gbm and catboost plsss sir apko dil se apna bada bhai maanta hun agar apne nhi banayi video toh main samaj jaoga ki aap mujko chota bhai nhi maante

[sunitaskitchen]

please make a video on LR classifier trained with ABC algorithm

[syedalinaqi]

When will you make video on Bert ? I recently completed watching your NLP playlist and now I am waiting for the bert session.

[rambaldotra]

Explained everything like Butter. Superb Sir.

[shubhamchoudhary]

Sir, in machine learning suppose if the model is overfitted & we apply k-fold cross validation on top of that model k-fold(10), then we get to see lots of variation in cross validation scores right ?.
Such as
Score = [ 40, 80, 70, 80, 50, 83.2, 81, 84
Means, here diffn between highest & lowest accuracy is ( 84 - 40 = 44) which is very high, means our model is overfitted & not capable to perform on unknown data situation, hence we need to regularise it &do hyperparameter tuning
.
Make me correct ..
Thank you!!!!..

[eramitjangra]

train test split also has option of stratify will that be useful for imbalance dataset

[MrKevZap]

7:22 Aren't we supposed to use X_train and Y_train instead of X and y in the parameters of cross_val_score function?

[rohitjagdale]

Thanks for the video. Please make video series on pyspark Mllib.

[shilpaprusty]

Sir please mAke videos on Bayesian optimization

[sachinpatil]

Sir please same me ...agar Mai python sekh rahe hua to uska liye front end aane jarure hey Kya like HTML, css, JavaScript and frameworks... help me please

[aimen]

Hello Krish, can you make a session on how to create Ensemble Model(implementation)

[anshtandon]

Sir for the imbalanced Dataset, can't we first handle the imbalance and then do KFold test?

[vaibhavkhobragade]

Thank you @Krish for such a great explanation. I have a question, cross_val_score(model, X, y, cv = k_fold_validation) we are passing X and y from original data without using training data. We also assume that X and y have missing values. If we do imputation on X and y, is there any data leakage problem in cross-validation? if it is how could we handle it?

[pavankumarchahar]

Please upload a Video about different Imbalanced Dataset and how to handle techniques, Various feature engineering approaches etc.

[harikrishnakokkula]

hii sir, plz do a video on KD TREE, Ball tree

[gwapdamathtutor]

I hear doing cross Val for entire dataset is wrong . Can you explain why not using it in training set