In this lecture, 3rd point is "NO heteroscedasticity" , please do not confuse

@manushrivetal6098

2 жыл бұрын

3. Homoscedasticity is one of the most critical It states that there should be an equal distribution of errors. It is not Heteroscedasticity

best video on assumptions of OLS on KZread

Becoming huge fan of this channel..grt explaination

You explained it in the easiest manner possible! Thanks for sharing this, Sir 😊

@UnfoldDataScience

2 жыл бұрын

Welcome Gaurav.

Hi, Could you please answer how should we approach this situation in regression problem: The target variable is distributed in a biased manner(50% of the values lie in the range 0-300 and 30% in 300-500 and 10% in remaining 500-1000) , how will you approach such scenario?

Good explanation, Thanks

Clear explanation in a very less time . Thank you 😊👍

@UnfoldDataScience

3 жыл бұрын

Thanks Prachi. Keep watching.

Very precise and very informative!! thank you sharing for this,Sir...

@UnfoldDataScience

2 жыл бұрын

Thanks Nayan.

great video. we need videos like that sir, (Assumptions of other algorithms)...

@UnfoldDataScience

3 жыл бұрын

Thanks Salik. Will try to create.

Access Hindi, English courses here- www.unfolddatascience.com/s/store Plz register on the website

could you please answer my question What is the similarities and the difference between a generalised linear model(Glm) and gradient boosting machine(Gbm)?

Very Crystal clear explanation !! Hoping for more of such content.

@UnfoldDataScience

2 жыл бұрын

Thanks Deepak.

Great Explanation! So much clarity on concepts.

@UnfoldDataScience

3 жыл бұрын

Glad it was helpful Rohan.

I am actively searching job. Sometime I feel like I won't get a job but after watching your videos I feel really learned something and it's give some confident. Thanks for the video. Please keep sharing videos.

@UnfoldDataScience

3 жыл бұрын

Best of luck buddy. you will do it. mark my words. cheers!

Can u make the live demo video to check all of these assumptions on the data on dataset

You deserve more subscribers aman.. Your teachings are very good👍👍

@UnfoldDataScience

3 жыл бұрын

Thanks a lot Rahul.

how i can check assumption in multiple linear Regression with categorical independent variable?

clean,simple and precise explaination

@UnfoldDataScience

3 жыл бұрын

Thanks Neeraj.

You explanation is very good and easy to understand. Thanks for this awesome video

@UnfoldDataScience

Жыл бұрын

Glad it was helpful!

Hi Aman, I think we need strong justification for point 3,4,5. Why it should not happen??. I was asked in an interview and I was not able to justify point 3,4,5 . Could you please elaborate little more on these points.

Please make a video on assumptions of logistic regression

Very good explanations, keep it up.

@UnfoldDataScience

2 жыл бұрын

Thanks, will do!

Amazing content.

@UnfoldDataScience

3 жыл бұрын

Thanks a lot :)

Sir..please explain whether the points 2 and 6 are different or not? why?

So clear and informatic❤️

@UnfoldDataScience

3 жыл бұрын

Glad you think so Sai.

It will be really helpful if you can provide a video lecture in which you put all the assumptions to a test on a Kaggle dataset(any). Cheers.. great work sir..

@UnfoldDataScience

3 жыл бұрын

Hi Abhinav, All the assumptions in the test means?

@abhinavkale4632

3 жыл бұрын

@@UnfoldDataScience all the assumptions as in multicollinearity, normality of residuals, autocorrelation.. all these assumptions applied on real dataset (basically executing all the assumptions in python)

Lovely Brother ..Thanks

@UnfoldDataScience

2 жыл бұрын

Thanks a lot.

good way of explanation

@UnfoldDataScience

2 жыл бұрын

Thanks Nived

Please make a vedio on end to end projects of all the algos

finished watching

Thank u for this video, clean explanation, waiting for this. I want video on kaggle specially using submission file using smothe technique.

@UnfoldDataScience

3 жыл бұрын

Thanks Nagnath. Sure :)

Yor are always great

Good explanation! thank you!

@UnfoldDataScience

3 жыл бұрын

Thanks Sandhya.

Very much needed .

@UnfoldDataScience

3 жыл бұрын

Thanks a lot :)

Why are these called Assumptions? They seem to be the mandatory conditions to ensure better regression model...?

Thank you sir ♥️

@UnfoldDataScience

Жыл бұрын

Most welcome

Very easy to understand. Please provide a video about RMSE

@UnfoldDataScience

2 жыл бұрын

I have video of R squared, not specific to RMSE, will create one.

Good Explanation.

@UnfoldDataScience

3 жыл бұрын

Thanks Divyanshu.

Sir, shall I ask a doubt? Should we consider multicollinearity in multiple linear regression, on a time series financial data?

@UnfoldDataScience

3 жыл бұрын

Yes Srividya, if you are training a regression model.

should the data have homescedasticity or hetero? Other blogs says it should have homoscedasticity. Please clarify. Thanks

@UnfoldDataScience

2 жыл бұрын

No Hetero, that is what I gave example and said it should not happen - may be missed to write "NO" before hetero in 3rd point

Thank you so much for this wonder content. It was really helpful. In multicolinearity part, I have a small doubt. I understood through your example, it is better to remove one feature out of 2 if they are positively correlated. Does the same applies for negatively correated features too? I mean shall I drop one feature, in case two features are positively correlated?

@UnfoldDataScience

2 жыл бұрын

Thanks Anirban. Postive/Negative correlation does not matter, we should not keep two variables which are highly correlated.(any direction)

great explanation

@UnfoldDataScience

3 жыл бұрын

Thanks Sandipan.

In next video please tell us how to check these assumptions and how to correct the data to follow these assumptions

@UnfoldDataScience

3 жыл бұрын

Sure Neeraj, Will do.

My data has no multicolinerity ( value is 1) and is not normal. Can i run regression ?? Plz answer

@UnfoldDataScience

3 жыл бұрын

You can run but model may not be very robust.

you should explain why in the last three assumptions.

@UnfoldDataScience

2 жыл бұрын

Sure, thanks for feedback.

excellent explanation. can you demonstrate a linear regression?

@UnfoldDataScience

3 жыл бұрын

kzread.info/dash/bejne/aoR61paDmdGdcpM.html

Thanks I just have couple equestion . 1- What is the disadvantages of multicolinearity 2- in several cases, the distibutiin of error vector is not following the normat distribution. How can I deal with that

@AshokKumar-rh7ey

3 жыл бұрын

The same question i do have..

@UnfoldDataScience

3 жыл бұрын

I explained in multicollinearity video. kzread.info/dash/bejne/l5-pppqDhceyZrA.html

Thanks! Please explain 1st assumption

@UnfoldDataScience

3 жыл бұрын

Sure.

Will (or could) heteroskedasticity imply autocorrelation?

@UnfoldDataScience

2 жыл бұрын

here heteroskedasticity is in context of error terms. It will not mean autocorrelation.

How to remove multicollinearity from tha data set if the features are highly correlated can we solve the problem without removing any features or any information loss does PCA helfull?

@abhinavkale4632

3 жыл бұрын

I think we can use VIF "Variance inflation factor" and then decide which features should be included in the model. In addition, we also need to check the significance value from the OLS regression model. There is a threshold limit(generally for VIF

@arvindadari3390

2 жыл бұрын

PCA will definitely help to tackle the multicollinearity but will loose out Interpretability.

@UnfoldDataScience

2 жыл бұрын

To Answer question from Yash, there is very low information loss if you remove a highly correlated variable.

Awesome explanation. Thank you Aman. Have one question, isn't assumptions 3 and 4 are same Heteroscedasticity and No auto correlation of errors?

@UnfoldDataScience

2 жыл бұрын

No, not same,

@UnfoldDataScience

2 жыл бұрын

It's little long explanation, sorry to not able to write, will talk in live or interviews

@sowjiadabala

2 жыл бұрын

@@UnfoldDataScience Sir have you explained this in any of your videos ?

Can you help me with regression models with multi-dimensional data?

@UnfoldDataScience

2 жыл бұрын

Its there in my channel. Search with channel name and topic

Similar can we have any assumptions and limitations to logistic regression

@UnfoldDataScience

2 жыл бұрын

Yes Sharan. That comes also under regression umbrella.

sir can u please provide the notes

1. Linear relationship 2. Very low/No multicollinearity (independent variable correlation each other 3. heteroscedasticity 4. No autocorrelation Normally distributed error 5. All the observations are independent of the each other

@UnfoldDataScience

2 жыл бұрын

Thanks. 3rd point - " No heteroscedasticity"

Because of this question, I was rejected in final round interview of ZS-Data Science Associate😂

@UnfoldDataScience

2 жыл бұрын

Now that you have understood, you will do great 😊

Does it applicable for all regression model or only in linear regression models?

@UnfoldDataScience

3 жыл бұрын

All regression based model mostly.

Explanations is ok but Writing on the board is provide notes for the students it's my suggestion

Just a note. The relationship between dependent and independent variables should be linear, linear in terms of coefficients but not in variables. When we are doing polynomial regression, the linearity between variable with target will not hold true. As we have raised power terms.

@UnfoldDataScience

2 жыл бұрын

Thanks Arvind.

what is mean by linear relation? this is the interview question

@UnfoldDataScience

3 жыл бұрын

Linear relation means y = mx+c kind of relation

Is this a assumption of OLS

@UnfoldDataScience

2 жыл бұрын

Yes. OLS is the internal methodology for Linear regression(If we don't use gradient descent).

What are assumptions for Classification?

@UnfoldDataScience

2 жыл бұрын

if its logistic regression u are asking about it will be mostly same

I got this question today and couldn't answer other assumptions! I felt really very bad about myself

@UnfoldDataScience

2 жыл бұрын

Np. it happens witj all of us. Keep trying.

@RajdeepBorgohainRajdeep

2 жыл бұрын

@@UnfoldDataScience thanks for motivating Aman :)

Not able to get a job in field of Data science. Dont know what to do.. Frustated !!

@UnfoldDataScience

3 жыл бұрын

Hi Shreyash, lease make your resume strong. Learn new stuff, approach people for more opportunities also.

You are extremely good in teaching I am looking for. Can I have your email address? I am from Bangladesh, beginner in research (M.Phil). I'm struggling in some topics of data analysis. I would like to contact with you if you approve. Thanks

@UnfoldDataScience

3 жыл бұрын

please connect on LinkedIn

can you explain by taking a real life example more deeply because whatever you explained are the basic things with no depth explanation, so if possible please explain deeply by taking a good example even if the video becomes longer

@UnfoldDataScience

2 жыл бұрын

Thanks for the suggestion, Will definitely take in consideration.

very generic explanation, u should cover them in a little detail

@UnfoldDataScience

2 жыл бұрын

Thanks for feedback, will try to cover.