Bro, if you ever feel or get a doubt in your life that you are not good teacher then, I would like to say that look yourself through my eyes and you will come to know what mark you left on my mind about this subject. Hats off, and I appreciate your work. Literally, I would have paid your fees but a "like" and taking "subscription" of your channel is all that I can do for you. I will definitely share this content to my friends. 🤘🤘
i am halfway in this beautiful journey of learning ML...and thank god i am here in this playlist...because i am sure there can't be a better playist ....
Your explanation was concise and really helpful for understanding the concepts. I don't usually comment but I highly appreciate your videos! Subscribed and liked.
You are really a blessing for us. Your explanation is way better than any of the other youtube channels. Thank you so much for the detailed explanation of these videos.
Hi Nitish. I must say your lectires are great man. I mean I cant really express the complete gratitude that I'm feeling at the core of my heart towards you. Especially when I see these all things in the essence of desi hindi. Great man. God bless you. One request..if you could add the Variance Inflation factor(VIF) as well into it. Though thats not very different from R2 Score but that has quite good application in Quant Finance relatrd data science
What a great explanation sir Thank you so much I am beginner in data science and i was stucked in one problem where i got r2 score as .99 but my submission getting so much error This helped me out from that scenario
Sir, we now often see in some analysis people showing MAE as 0.20 ± 0.011 as well as R2 as 0.45 ± 0.028, can you kindly explain how these ± values are obtained for MAE and R2 for any analysis can any of the python packages can derive these values. Thank you
What if I have my regression model - Model Performance: Mean Absolute Error (MAE): 0.2903 Mean Squared Error (MSE): 0.1684 R-squared (R2) Score: 0.9764
Sir , to according to you mujhe dub ke marna hi padega. Since my r2 score is -ve for linear regression model. But I achieves good r2 score for xgboost. For same dataset.
Just one point about MAE I would like to share that MAE can be negative and error shouldn't be negative therefore MAE shouldn't be trusted anyways for evaluation.
Bro, if you ever feel or get a doubt in your life that you are not good teacher then, I would like to say that look yourself through my eyes and you will come to know what mark you left on my mind about this subject. Hats off, and I appreciate your work. Literally, I would have paid your fees but a "like" and taking "subscription" of your channel is all that I can do for you. I will definitely share this content to my friends.
🤘🤘
500% agree brother
This series is one of the best series on internet for Machine Learning
Yes
Agree
i am halfway in this beautiful journey of learning ML...and thank god i am here in this playlist...because i am sure there can't be a better playist ....
I also
Actually there none
maja aa gaya sir! dil se thank you!
you, krish naik and statquest's josh starmer are the only teachers we need in life.
Generally I can"t comment on videos.
But your machine learning list is really helpful for me.
Great And mind blowing session.
This lecture is a goldmine! 🙏
diamond mine 😅😅
Honestly i will say , best ever explanation... Matlab Aisa itna acha TH-cam pe expalin hi Kiya nai kisine ... Thank you so much.🙏
literally mind-blowing session, no body could explain these convoluted topics like this... hats off.
Your explanation was concise and really helpful for understanding the concepts. I don't usually comment but I highly appreciate your videos! Subscribed and liked.
You are really a blessing for us. Your explanation is way better than any of the other youtube channels. Thank you so much for the detailed explanation of these videos.
Thank you, sir. I will try to convert all this explanation is a blog.
I just started with Machine Learning and you really help a lot in understanding concepts. Thank you
hands down better than Andrew NG, i never go for learning on youtube, always took authentic courses but omg this series is just mind blowing
Super duper hit teacher in this whole world....
I Appreciate your work .........
probably the best video out there on this topic. Best Teacher Ever!
Hi Nitish. I must say your lectires are great man. I mean I cant really express the complete gratitude that I'm feeling at the core of my heart towards you. Especially when I see these all things in the essence of desi hindi. Great man. God bless you.
One request..if you could add the Variance Inflation factor(VIF) as well into it. Though thats not very different from R2 Score but that has quite good application in Quant Finance relatrd data science
Awesome video sir, I have become fan of you, the way you teach is incredible. Thank you for putting such a valuable content.
One of the best video series on TH-cam for Machine learning
We are not going to code MAE, MSE, RMSE, R2 and adj R2 because it is not about programming; it's about Data Science. 34:42
This Sentence ❤
These explanations are better than books!
Awsome explaination. This channel has great videos . Thanku so much sir.
Please do video on assumptions of linear regression
Best video on R2 and Adjusted R2..keep up the great work sir.
when r2 score is -ve -> doobh ke maar jao ,very smooth sir, it caught me 🤣🤣🤣 ,Overall lecture was very good👍👍
*Robustness to outliers* refers to the ability of a statistical method or estimator to remain relatively unaffected by extreme values in the data.
Thank you sir ❤❤
hey man a little correction in 23:30 , the ssm is not y-y^ . It is y-ymean
Thanks
y(mean) in place or ŷi in the denominator instead of [....]m feels more intuitive... what is you view?
great explanation better than even best professors, but please check error in r^2 formulae written
I m learning lot of concept from ur videos and enjoying ur video . Appreciate ur work. Dil se dher sara pyar bade bhai
23:39 LOL.....nice explanation sir!!!
Sir, you are a real gem !!
What a great explanation sir
Thank you so much
I am beginner in data science and i was stucked in one problem where i got r2 score as .99 but my submission getting so much error
This helped me out from that scenario
Very Nice bro , first time got a better explanation. Understood in better way . 🤟
Awesome explaination bro🎉
Thank You Sir.
can you also make a video on assumption of Linear Regression, that is most asked question in interviews and hard to understand?
Does the phrases like "penalizing the outliers" and "robust to outliers" are same in meaning or they are different?
timestamp 23:38 was so hilarious that it made me laugh so loud while watching at night 1 am.
your concept and way of giving knowledge are really good and help fulll
to bingers
Please Continue deep learning.
Thanks a lot
you are the life saver. thank you so much for in detail explanation.
wow what an explaination, loved it. Thank you
Thank you so much for your efforts. I'll definitely pay you off when I get my first salary. Thank you so much
Amazing explaination
concept clarity is what "campus x" means
You said MSE is "robust" to outlier, I think it should be "sensitive" to outliers.
He said MSE is "not robust" to outliers
42.57-- the adjusted r2 score shld increase na??? As we have added iq column, but why it has decreased??? as compare to r2 score??
Sir, ye sare metrics values ko improve kaise kare, koi lecture hai kya isspar??
R2 square is numerically equal to correlation between y and y cap !?
Best Explanation.
Sir, we now often see in some analysis people showing MAE as 0.20 ± 0.011 as well as R2 as 0.45 ± 0.028, can you kindly explain how these ± values are obtained for MAE and R2 for any analysis can any of the python packages can derive these values. Thank you
Is it important to do train test split and then perform feature engineering or you can do feature engineering and then train test split
Very clearly explained thank you sir
Thanks a lot for these wonderful stuff
anyone while learning ML algorithms, should i also try to build some projects simultaneously?
sir R2 score is affected by outliers also , so in such case what we can do?
Thank you so much sir 🙏🙏🙏
What if I have my regression model -
Model Performance:
Mean Absolute Error (MAE): 0.2903 Mean Squared Error (MSE): 0.1684 R-squared (R2) Score: 0.9764
Excellent Explanation ❤🙌
Very nicely explained, thank you sir
Best explanation 👌
very nice content, made things clear
Sir how to test accuracy score for train and test data to know underfit or overfit model, can someone help on this.
Thanks best video🎉
sir can we drop columns of larger dataset based on r2 score or not ?
Brilliant explanation. Thanks a lot
r2 negative aarha hai, doobke mrjao😆😆. loved it
very nice explanation sir thank you
sir indeirectly roasting their students 23:44 😂
Can we improve linear regression algoritham using hyper parameters?
nhi bhai , bilkul bhi nhi
Sir , to according to you mujhe dub ke marna hi padega.
Since my r2 score is -ve for linear regression model.
But I achieves good r2 score for xgboost.
For same dataset.
Nice 🤠
the best💗💗💗💗💗
Thankyou sir , God bless you.
Doob ke marjav....wah sir kya dialogue hai..😂😂😂😂
Wow! Awesome explaination of r2 score
wah 💯👍
amazing vid
Clear kr diya bhai
Just one point about MAE I would like to share that MAE can be negative and error shouldn't be negative therefore MAE shouldn't be trusted anyways for evaluation.
finished watching
If u really want to learn & understand machine learning , only Campus X.
Thank you sir.
Isn't R2score = 1 - ssR/ssT ?
Depends on what you mean by SSR. Sum of Squares Regression or Residuals?
Thank you sir♥️
Thank you so much.
I think R2 me 1- residual/mean hona chaiye,
Can I use MAE for the binary data???
No
Beautiful
Pls make videos in English
Sir mera R2 score itna negative aaya ki dataset khud bola ki bhai tu rehne de 😭😭😭😭😭😭😭😭
thank u sir
you are great
Thank you :)
Thanks A Lot Sir
Most welcome
Besttttt
23:44 agr r2 score negative araha hn tu doob ka mar jaoun. 😂😂
Legend spotted