Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score
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- Опубліковано 21 лип 2024
- Understand key metrics for evaluating regression models in this video. We cover Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R2 Score, and Adjusted R2 Score. Learn how these metrics help assess the accuracy and performance of your regression models.
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⌚Time Stamps⌚
00:00 - Intro
01:32 - MAE(Mean Absolute Error)
07:41 - MSE(Mean squared Error)
12:29 - RSME(Root Mean Squared Error)
15:52 - R2 SCORE
27:06 - Adjusted R2 SCORE
33:41 - Code for the above Matrix
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.
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Yes
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I also
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literally mind-blowing session, no body could explain these convoluted topics like this... hats off.
probably the best video out there on this topic. Best Teacher Ever!
One of the best video series on UA-cam for Machine learning
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.
Best video on R2 and Adjusted R2..keep up the great work sir.
Awesome video sir, I have become fan of you, the way you teach is incredible. Thank you for putting such a valuable content.
I just started with Machine Learning and you really help a lot in understanding concepts. Thank you
Generally I can"t comment on videos.
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Great And mind blowing session.
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Thank you, sir. I will try to convert all this explanation is a blog.
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
Awsome explaination. This channel has great videos . Thanku so much sir.
wow what an explaination, loved it. Thank you
Very Nice bro , first time got a better explanation. Understood in better way . 🤟
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
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to bingers
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Amazing explaination
Awesome explaination bro🎉
Best Explanation.
Brilliant explanation. Thanks a lot
Very nicely explained, thank you sir
very nice content, made things clear
Very clearly explained thank you sir
Excellent Explanation ❤🙌
Thank You Sir.
Thankyou sir , God bless you.
very nice explanation sir thank you
Does the phrases like "penalizing the outliers" and "robust to outliers" are same in meaning or they are different?
I m learning lot of concept from ur videos and enjoying ur video . Appreciate ur work. Dil se dher sara pyar bade bhai
great explanation better than even best professors, but please check error in r^2 formulae written
Best explanation 👌
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 ❤
Is it important to do train test split and then perform feature engineering or you can do feature engineering and then train test split
amazing vid
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
concept clarity is what "campus x" means
You said MSE is "robust" to outlier, I think it should be "sensitive" to outliers.
Thank you sir.
Clear kr diya bhai
Thanks best video🎉
sir can we drop columns of larger dataset based on r2 score or not ?
Thank you so much.
when r2 score is -ve -> doobh ke maar jao ,very smooth sir, it caught me 🤣🤣🤣 ,Overall lecture was very good👍👍
timestamp 23:38 was so hilarious that it made me laugh so loud while watching at night 1 am.
sir R2 score is affected by outliers also , so in such case what we can do?
R2 square is numerically equal to correlation between y and y cap !?
23:39 LOL.....nice explanation sir!!!
thank u sir
you are great
Sir, ye sare metrics values ko improve kaise kare, koi lecture hai kya isspar??
Thank you sir♥️
Beautiful
hey man a little correction in 23:30 , the ssm is not y-y^ . It is y-ymean
Thanks
Sir how to test accuracy score for train and test data to know underfit or overfit model, can someone help on this.
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??
wah 💯👍
the best💗💗💗💗💗
Thank you :)
finished watching
Besttttt
Wow! Awesome explaination of r2 score
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
Thanks A Lot Sir
Most welcome
Doob ke marjav....wah sir kya dialogue hai..😂😂😂😂
Can we improve linear regression algoritham using hyper parameters?
nhi bhai , bilkul bhi nhi
r2 negative aarha hai, doobke mrjao😆😆. loved it
🔥🔥
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.
Can I use MAE for the binary data???
No
Legend spotted
❤
doob ke mar jao hahahahaha sir kash me apko hug kr pata ... you are best teacher in the world...
If u really want to learn & understand machine learning , only Campus X.
Isn't R2score = 1 - ssR/ssT ?
Depends on what you mean by SSR. Sum of Squares Regression or Residuals?
Pls make videos in English
love for Pakistan
23:40 :D
Sir mera R2 score itna negative aaya ki dataset khud bola ki bhai tu rehne de 😭😭😭😭😭😭😭😭
6:41 😂😂
23:39 best part ; doob ke Marjao hahaha
🏊♂🏊♂ swimming nahi aata hain
what was that "Doob k mar jao 😆😆😆😆😆".
@
dum ke mar jao😂😂😂😂