Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score
ฝัง
- เผยแพร่เมื่อ 21 ก.ค. 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
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I just started with Machine Learning and you really help a lot in understanding concepts. Thank you
One of the best video series on TH-cam for Machine learning
probably the best video out there on this topic. Best Teacher Ever!
Best video on R2 and Adjusted R2..keep up the great work sir.
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Great And mind blowing session.
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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
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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.
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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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I Appreciate your work .........
Awsome explaination. This channel has great videos . Thanku so much sir.
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Amazing explaination
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 ❤
maja aa gaya sir! dil se thank you!
Awesome explaination bro🎉
Brilliant explanation. Thanks a lot
Best Explanation.
Very nicely explained, thank you sir
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very nice content, made things clear
great explanation better than even best professors, but please check error in r^2 formulae written
Very clearly explained thank you sir
Is it important to do train test split and then perform feature engineering or you can do feature engineering and then train test split
Excellent 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
Does the phrases like "penalizing the outliers" and "robust to outliers" are same in meaning or they are different?
very nice explanation sir thank you
concept clarity is what "campus x" means
sir can we drop columns of larger dataset based on r2 score or not ?
You said MSE is "robust" to outlier, I think it should be "sensitive" to outliers.
Best explanation 👌
Thankyou sir , God bless you.
sir R2 score is affected by outliers also , so in such case what we can do?
Thank You Sir.
R2 square is numerically equal to correlation between y and y cap !?
Clear kr diya bhai
Sir how to test accuracy score for train and test data to know underfit or overfit model, can someone help on this.
Sir, ye sare metrics values ko improve kaise kare, koi lecture hai kya isspar??
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
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??
Thanks best video🎉
Thank you sir.
timestamp 23:38 was so hilarious that it made me laugh so loud while watching at night 1 am.
amazing vid
when r2 score is -ve -> doobh ke maar jao ,very smooth sir, it caught me 🤣🤣🤣 ,Overall lecture was very good👍👍
Thank you so much.
Wow! Awesome explaination of r2 score
thank u sir
you are great
23:39 LOL.....nice explanation sir!!!
Thank you sir♥️
Beautiful
wah 💯👍
finished watching
Thank you :)
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Besttttt
hey man a little correction in 23:30 , the ssm is not y-y^ . It is y-ymean
Thanks
Thanks A Lot Sir
Most welcome
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.
r2 negative aarha hai, doobke mrjao😆😆. loved it
Can we improve linear regression algoritham using hyper parameters?
nhi bhai , bilkul bhi nhi
🔥🔥
Can I use MAE for the binary data???
No
Legend spotted
Sir mera R2 score itna negative aaya ki dataset khud bola ki bhai tu rehne de 😭😭😭😭😭😭😭😭
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Isn't R2score = 1 - ssR/ssT ?
Depends on what you mean by SSR. Sum of Squares Regression or Residuals?
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Pls make videos in English
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