Hi Bhavesh, I've always found your content to be very accurate and informative, thanks a lot for putting in the effort. In this particular topic, do you mind sharing how to do course correction if any of these assumptions are not fulfilled ?
Thanks, Bhavesh. Very nice video. I really enjoyed it. Do you mind doing a follow-up video of how to address the issues if the features don't follow these assumptions.
Sir, I am facing issues with Homoscedasticity and Autocorrelation greater than alpha. Homoscedasticity: Residual value remain constant with increase in predicted value. What should I do?
If your data was randomly collected (no biases which will be the majority of cases in real life when data is collected), then automatically the homoscedasiticity assumption is met.
You should also teach if we found any of the assumptions true than how to solve it
AMAZING. I was not able to find examples of assumptions anywhere else in python. All the thanks Bhavesh.
Thanks for sharing Bhavesh!
My pleasure
Very helpful ... Nice
Glad it helped
Just Saw this video link on Linked In as you are in in my network and vedio is understandable and worth to watch
Amazing Work Man....
Thanks a lot!
Super.. keep doing more videos. Thanks a lot 🙏🏻
Thank you!
You're welcome!
how we can check starting itself that there is linear relationship or not between dependent and independent variables?
Hi Bhavesh, I've always found your content to be very accurate and informative, thanks a lot for putting in the effort. In this particular topic, do you mind sharing how to do course correction if any of these assumptions are not fulfilled ?
sir can you please clarify whether we check for assumptions on the training set or test set?
Thanks, Bhavesh. Very nice video. I really enjoyed it. Do you mind doing a follow-up video of how to address the issues if the features don't follow these assumptions.
I'm glad you liked the video Upendra! Sure, I'll create a follow-up video soon!
What to do when residuals are centred around 0 but mean =78 and there are outliers in residuals
Thank you soooooooooooooooo much
Hi Bhavesh Bhatt, Doing so well man,
Suppose if my model performs well by accepting 3 out of 5 Assumptions. Can I use that model to do further steps?
Can you make a video on Autocorelation ?
Hi Bhavesh, Thanks for the video
My Data has Ordinal Independent variable, and Interval/Ratio Dependent variable
How to do Encoding Features?
If features do not follow some of assumptions, should we make the variables to follow assumptions. eg normality
why we need to check to wither Linear Regression fellowing Assumptions or Not. when it required.
Is this assumptions are same whe im using scikit learn in linear regrations
Algorithms remain the same, packages keep changing!
Hi
Could you please make a playlist for deep learning??
Sure! I'll make a playlist on Deep Learning soon.
@@bhattbhavesh91 thank you
Sir, I am facing issues with Homoscedasticity and Autocorrelation greater than alpha.
Homoscedasticity: Residual value remain constant with increase in predicted value.
What should I do?
If your data was randomly collected (no biases which will be the majority of cases in real life when data is collected), then automatically the homoscedasiticity assumption is met.
I am getting a huge VIF values.