Timeline of Assumption of linear regression 0) 00:51 Introduction 1) 01:58 Linear relationship (between all the independent and dependent features) 2) 04:25 No multicollinearity (between independent features) 3) 11:37 Normality of residuals (Distribution of residuals should be normal) 4) 13:15 Homoscadascity (residuals and predicted values should not have any pattern ) 5) 15:32 No autocorrelation of residuals

Sir aapse accha koi nhi samjha sakta ❤️

Sir if possible make video on AUC ROC curve...and thank you for making this video

Sir aapne bohot hi badhiya padhaya ....after watching a lot of videos but not founded a video like this ....now i understood the concept only by you ...ty

This is what content is. I hope you will give deep understanding on other topics too

thanku bhai the most "to the point" and easiest explained video in youtube.

Very Helpful Video for people who grasp a concept from fundamentals. Very intuitive with practical implementation.

ON THE POINT!!!!! VERY IMPORTANT INFORMATION REGARDING INTERVIEWS

Yours videos are very well explained ,Thank you soo much Sir for giving the knowledge. You are the best teacher ever

The only video on YT that explains assumptions of alogorithm. Thank you so much sir this video was a great help. Sir, can you please make videos like this for other alogorithms also.

One of the best and quick video I have seen

Sir,ur teaching skills is very awesome ❤️It was much helpful for me Thanku 💐🎉

you simplified the concept..Thank youuuuu

Dude, you are really a genius......excellent explaination

Great explanation sir,simple illustrated by example

Best teacher ever!

This Question has been asked in Turing data scientiest interview Sir, thank you so much.

Thank you for this wonderful information🎉

Thanks you for simple and great explanation

thank you clearly explained the concepts

Thank You Sir.

Thank you. Very helpful

Amazing Knowledge Sir.

Thanx for clear explanation it was quite informative

Thank You Sir, the way you make us understand is really great... Love From Pakistan 💖

Its very very good lecture sir thanks a lot

Thankyou Sir. I will reference this content.

Good Video on Assumptions of Linear Regression🙂

great explanation... thank you! 😇

Very nice explanation❤

Very well sir, thank you so much 😊

1:44 During train_test_split, the rows of the data are randomly ordered (unless you set a parameter not to reorder, which is not set here). Because of this, the residuals at 16:08 will always show no auto-correlation even if it was, as the order is jumbled up.

Please make video on how to overcome each assumption if it is invalid

Hello Sir, As always this video is also amazing no doubt about that. But in this video you only explained How to check the assumptions. But what if the assumption is not hold than how to tackle them ?? Like what are the processes in order to convert the data so that it holds all assumptions. please make video on that and explain that.. Thankyou Sir

Nice video sir Thank You So Much

deep learning video sir!

Thank you sir ♥️

Thank u sir

Very nice explanation sir

Thank you sir❤❤

thank you so much sir

Thanks jitu bhaiya.

Well Explained

Sir in Autocorrelation of Residuals if we sort the data then it will also follow some pattern. This plot depends on the order of input and we can pass input in any order. btw great video. Thanks

Nice explanation

sir videos is very good , sir we need a videos for that case if assumption not satisfy how we can use remedy of these assumption in python

Thankyou sir 🙇

thanks sir

Very nice ...

Thanks bro

Same Question asked me. Can you explain all assumption of all algorithms?? Plz sir that will be very helpful for us. 🙏🙏🙏🙏

Great Explanation.. But can you please make a video on detail explanation of autocorrelation and homoscedasticity??.. Thank You

bravo!

Thank Men

NIcely Explained

Awesome 👏

good one sir

Nice video sir.., the no autocorrealtion assumption is only for linear regression or it is applicable for other algorithms

Hi Nitish there is one more assumption for this our response variable should be normally distributed please explain thats why we use GLM

Conceptually what does auto correlation of residuals represent? You explained nicely why there should not be a correlation b/w Independent variables. But I didn't understand significance of no-auto correlation assumption for residuals

Sir please keep making Deep learning videos for the 100 days ml playlist

1. Linear relationship between input and output 2. No multi collinearity 3. Normality of residual 4. Homoscedasticity 5. No auto correlation in residual

Sir i am facing a bimodal residual issue or problem dont know what to say. Even my teacher dont helped me in that. Can you give some points or anything

Excellent content Sir but I have a doubt. Residual should be bell-shaped then how it's not holding any auto-relation correlation?

Hi sir, The 1st assumption of linear regression is that the equation should be linear in parameters and there is no restriction on how x and y are related. But u showed in ur video that if there is non linear relationship between x and y then the equation doesn't holds the assumption which I think is not right.

Make video on What to do if these assumptions get violated

In both assumptions 3 (normal residual) and 5 (autocorrelation) , we are plotting residuals, How come assumption 3 says it is normally distributed but 5 says there is not relation?

sir plz Deep Learning and NLP ki playlist complete kr do.

🎉🎉🎉

Sir, aapne bataya tha January me NLP ka playlist khatam hoga ! Abhi June khatam hone wala hai 😞

Sir apne btaya how to check linearity, but ye nahi btaya agar non linear h to krna kya h

nice

thank you sir , apne iski githhub link di hoti to time save hota hamhara...

The only missing thing was the "why" Why do we need these assumptions of linear regression. You only explained Multicollinearity would have been perfect if explained for all.

What if these assumptions get violated ?

SIR how can I join your online 6 month Ml &AI COURSE?PLEASE REPLY SIR.Thank you🙏🏻

hello there. As u said these are the assumptions in LR and a candidate who is not aware of these is judged. But the thing is, from where can one read about such concepts?. Can you please suggest some books with solid ml fundamentals as there is a lot of ambiguity about concepts in ML books and not every book talks in depth about these algorithms.

@campusx-official

2 жыл бұрын

www.amazon.in/Elements-Statistical-Learning-Prediction-Statistics-ebook/dp/B00475AS2E

It would have been a good video , if the reasons behind these assumptions is well explained. the reasons behind the Normal Residual, Homoscedasticity, No Autocorrelation of Error are not explained. how does these assumptions impact the model is not explained. Thanks for explaining the meanings of these errors with examples.

Isn't the assumption about linearity in the sense that it should be linear in parameters, not variable? That is to say the assumption is fine with non linearity in X variables until coefficient (ß) of X is linear.

I would suggest you to explain why linear regression assumes, normal residuals, homoscdacitiy and no correlation between/w residuals/independent variables. That will help make your channel different from others because it will help your audience understand the concept better. The things you have explained, anyone can explain it, but only a handful number of people explains "why"

@arun5351

2 жыл бұрын

You can explain the reasoning behind it. Others can also chip in. Nitish can correct our understanding if there are any gaps.

Sir how to handle multicollanirity??? should we drop one column???

@tanmay_efootball

Жыл бұрын

yes , we have to drop (highly i think )

LR No multicollinearity Normality of residuals Error should have constant variance No auto correlation of errors

Can you make tutorial on deep learning

@campusx-official

2 жыл бұрын

100 Days of Deep Learning: kzread.info/head/PLKnIA16_RmvYuZauWaPlRTC54KxSNLtNn

@ParasProgramming123

2 жыл бұрын

@@campusx-official thank you sir. Sir is macbook air m1 good for such work such as ml and dl I am watching your 100 days of machine learning and reached day 3 because it been only 3 days of me starting this new journey

but why these assumptions??

You should actually rectify yourself. The linear assumption is never about straight line... it is linear in estimation parameters. y= k x^2 is also linear regression. Please correct.

Wasted 19 minutes . You should explain the reason for having these assumptions

@surajvishwakarma4534

Жыл бұрын

🤡🤡

agar result 1 se 5 ke bitch mai aarahe ho to "multicolinearity hai ya nahi"???????🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🙄🙄🙄🙄🙄🙄🙄🙄🙄

Thank you sir

Thnks you sir... 👍

amazing sir