Marvellous Video,got all the concepts cleared.

Simply outstanding!

awesome explanation. thanks a million.

Thank you for posting this! It help me quite a bit.

what a great video Thank You Sir

amazing video, I was so confused by this

Great video! Very helpful for my data analysis project

It is a helpful video. Many thanks, Prof.

good video, really helpful, thanks

thankyou for such a nice explanation.

Thank You So Much Sir it's very clear and understandable

Very good explanation, understand upon your explanation. !

Very fine and understanding vdo

Very very well explained. Thankyou ❤️ from 🇮🇳

amazing

Thank you sir.

The best on yt

Hi Dr Ryan, If my R2 is 0.30 and my RMSE is relatively very low, consider the RMSE below the average, so should I consider this model to be good.

Thank you!

Thank you so much

great! thanks Prof.

good day sir, I just wanted to ask if an independent variable is not significant or does not have an explanatory power to the model but when removing it lowers the adjusted r-square what does this imply? so far the reason that i know the reason is because the t-statistic is greater than one. With this information, what can we infer?

It would have been great if the links to the related video were on the description.

👍👍

but how does it identifies as a useful or useless predictor ?

A good video but what most of you guys are not clarifying is , how do i know the model is good using the adjusted r2 . Like what is good adjusted r2? I have run the multiple regression on excel already with so many independent variables. so am stuck now, i cant comment whether its a good model or not using the adjusted r2. From which value to which value ? Thank you for responding

what is predicted r2

How do you know if the new variables added are not correlated to the target feature? If adding a variable to the equation increases the R2 value, isn't that evidence that there is potential correlation?

so R2_adj is always lower than the R2

Great video! However, why does the R^2 value increase with the number of independent variables?

@jiaminghu349

2 жыл бұрын

Intuitively you are adding more variables to the model so the model would have a better explanation of the data. At the least, R^2 won't decrease because you can set coefficient of new-added variables to 0 if it "harms" the model. But in general though useless, those variable would still have minor explanation of the data so R^2 would increase. But that often leads to overfitting, consider extreme case where you have as many independent variables as data points and the model is able to fit every single one of data point, and R^2 is 1 in that case, nevertheless it would be a useless model.

I have heared from my professors adjusted r2 value is always less than r2 value.why is it so?

@vikramsharma720

3 жыл бұрын

Since it overcomes the dependency i.e. misleading accuracy by inc in independent variables that is why this might happens.

there may be a mistake, I think R2 can be less than zero, right? Model is worse than average guess?

Which of the following tells us how strong the relationship is between two variables? IS THE ANSWER E?! a) the slope of a line b) the intercept of a line c) the coefficient of determination d) the coefficient of correlation e) both C and D are correct

Thank you for the beautiful video :-) What is your name by the way :-)

Sathi baneko xu waps garnus la

I generally write R-squared lol

capo

Thank you so much