The value that you put out for free is INCREDIBLY appreciated. You are seriously helping so many students and professionals through your videos. Thank you so much on behalf of all of us.
As always Ritvik never disappoints when it comes to breaking down a concept without relying on mathematical equations, and still giving the best overview of a concept in the most generalized way possible. Thank you!
I don't think I've ever commented on an educational video in my 22 years cause I'm always left with a doubt at the end, but this video genuinely helped me understand the terms in detail! Thank you ritvik
The best video on internet about Bias Variance. Since I am interested in machine learning, I have watched hundreds of video on this topic, but haven't understood much. But your video made it easy for me. You saved my life. Thanks a lot man. I pray for your good health & wealth
You are AWESOME... The fun part is, I grasped this concept earlier from some text but when I was reading some other resources about some other topics they also brought up the bias variance. From those explanations, I got completely confused and started to doubt my understanding itself. Thanks for your effort. It really helped me bring back my confidence.
I think you nailed it by clearly showing how model prediction vary based on the training data. Most bias-variance explanations out there never really make it clear that you're looking at the same model trained on different data Great job!
Just wanted to say thank you. All of your videos I have watched are pretty understandable. I'm reviewing those terminology concepts and prepare my coming interview. If it's possible, I'm looking forward to seeing your video about L1 and L2 explanation or the overfitting solution.
The pen-toss - finger-snap combo at the end was fun. 😄 Truly appreciate this succinct summary of the concept; served as a wonderful refresher. Saving this in a revision playlist.
1200 long page book on Practical and real world scenario based book on data science and machine learning. Download sample pages from the below product page. #Datascience #machinelearning #python #interview #interviewquestions Check out my latest product: DataScienceMachineLearningQuestionBank A well curated and catalogued practical based data science and machine learning questions and solutions.A must for all those aspiring and practising data scientists and ML engineers to deepen your... payhip.com
Bagging and Boosting ❤️ Can u please part 2 for this video with mathematics bcoz you really are good at explaining complex things I understood pca fully bcoz of u
Write a book about simply explained data science. You're great at explaining things intuitively and am sure you'll have a market for such a thing. Thanks for your vids bro.
FFS my university is filled with world famous research professors that dont know how to teach and couldnt explain this concept in hours of lecture :( thank you so much!
Thanks for the great video! One question Sir, do you also have a video of mean square error decomposing to Bias + Variance? I am confused in why some expectation are constant, and what the expectation over of. Thank you so much!
Thank you for the video, great job! But I have a question that based on my understanding, variance here should be the difference between prediction accuracy from different test data set. Please correct me if I am wrong. Thanks again for the video, love it.
Great presentation! You mentioned that complex models have a tendency to pick up both real patterns and noise from the dataset they are trained on, so their predictions are all different (high variance) due to the noise, but correct on average. I'm wondering, if you have a very large dataset, why isn't it viable to train the same very complex (strongly overfitted) model on N chunks of the dataset and use the average prediction of these N models?
Few variables, consistent prediction across datasets, average prediction does not get closer to true value. Many variables, inconsistent prediction across datasets (as predictor function models the idiosyncratic noise in the data - leading to high variance in predictions or prediction decisions) , average prediction gets closer to true value (as noise from individual datasets cancel each other - leading to low bias or error between the average across predictions and the true value).
The value that you put out for free is INCREDIBLY appreciated. You are seriously helping so many students and professionals through your videos. Thank you so much on behalf of all of us.
As always Ritvik never disappoints when it comes to breaking down a concept without relying on mathematical equations, and still giving the best overview of a concept in the most generalized way possible. Thank you!
I don't think I've ever commented on an educational video in my 22 years cause I'm always left with a doubt at the end, but this video genuinely helped me understand the terms in detail! Thank you ritvik
Oh my days, I struggled for the past 3 days trying to understand this concept. It all makes sense now! Thanks a ton!
Hey Ritvik, I like the way you try to build up the intuitive sense around maths rather than focusing on the theorems! Great work!
One of the clearest explanations of this tradeoff I've seen so far, thanks!
The best video on internet about Bias Variance. Since I am interested in machine learning, I have watched hundreds of video on this topic, but haven't understood much. But your video made it easy for me. You saved my life. Thanks a lot man. I pray for your good health & wealth
You are AWESOME...
The fun part is,
I grasped this concept earlier from some text but when I was reading some other resources about some other topics they also brought up the bias variance. From those explanations, I got completely confused and started to doubt my understanding itself.
Thanks for your effort. It really helped me bring back my confidence.
Glad it was helpful!
I think you nailed it by clearly showing how model prediction vary based on the training data. Most bias-variance explanations out there never really make it clear that you're looking at the same model trained on different data
Great job!
Appreciate the video. What a intuitive, straight-to-the point lecture in a perfect play time
Thank you so much. This is by far the clearest explanation I have ever come across on this topic!
Just wanted to say thank you. All of your videos I have watched are pretty understandable. I'm reviewing those terminology concepts and prepare my coming interview. If it's possible, I'm looking forward to seeing your video about L1 and L2 explanation or the overfitting solution.
Hey bro.
You make excellent videos. Keep up the good work.
Thank you so much 😀
The pen-toss - finger-snap combo at the end was fun. 😄
Truly appreciate this succinct summary of the concept; served as a wonderful refresher.
Saving this in a revision playlist.
I really enjoyed this explanation. This is a must watch for anyone who wants to start working with machine learning.
Glad you enjoyed it!
1200 long page book on Practical and real world scenario based book on data science and machine learning.
Download sample pages from the below product page.
#Datascience #machinelearning #python #interview #interviewquestions
Check out my latest product:
DataScienceMachineLearningQuestionBank
A well curated and catalogued practical based data science and machine learning questions and solutions.A must for all those aspiring and practising data scientists and ML engineers to deepen your...
payhip.com
And here I am - coming back to your videos even after finishing an ML course.
Thank you
This is the only video, I learnt about bias-variance tradeoff
Wow!! Very well explained!! I appreciated your effort. It is not easy to put all these together perfectly! Thank you so much!
Wonderful video! Never really understood these terms when studying until now :) Thanks a lot!
Bagging and Boosting ❤️
Can u please part 2 for this video with mathematics bcoz you really are good at explaining complex things I understood pca fully bcoz of u
You are an absolutely incredible teacher!
Made this crystal clear. Thank you for this content
One of the best DS tutors out there. Keep it up!
Glad you think so!
This makes so much sense! Thank you for the awesome explanation.
This was a fantastic explanation. Thanks for the clarity!
Love such an easily explained complex stuff! Thx a lot!!!
Thank you so much....this was one of the best explanation of variance- bias...again Thank you so much for all your videos...Respect...
This is the best youtube video I've ever seen. Thank you so much
Wow, thanks!
Pleasantly surprised to see this good of content on youtube!
Thanks for simplifying such a complex topic!
Very well explained! Great work!
A wealth of wisdom in a nugget!
Hey @ritvikmath,
This man is genius. He explains the complex stuffs so simple.
Hats off sir.
"Obviously", this is the best explanation of bias and variance.
amazing video pure informations well done thanks ritvik
wow. You are so good! You really help me understand the concept completely!
I'm so glad!
Write a book about simply explained data science. You're great at explaining things intuitively and am sure you'll have a market for such a thing. Thanks for your vids bro.
Best explanation on this topic! Thank you!
Thank you so much! Great explanations, wish I would've watched this sooner
The explanation was super!!! Thanks for sharing this
That's one great overview! Thanks!!
FFS my university is filled with world famous research professors that dont know how to teach and couldnt explain this concept in hours of lecture :( thank you so much!
Simply explained is best for intuition
you are just GREAT dude... you explained it sooooooo nicely i cant believe
Great explanation Ritvik, thank you!
Awesome explanation. Sincere thanks
very clear about the definitions of variance and bias. It tells sth. about many models, not one.
Thank you very much this's exactly what I needed.
excellent presentation for boas and variance. Thank you
Yes, I did intuitively understood the concept. Thanks
Great explanation ! Thanks !
Thank you, what a great explanation
The wow part was the explanation on contribution of each model to learn the average 'signal' (true pattern) and 'noise' of a data.
Subtitles of this video are rally nice. Without it I wouldn't known you speak Korean.
My course book definition was so confusing but you made it so clear. Thank You!
Of course!
great explanation. thank you, master
Absolutely Perfect!!!
this is the best explanation!
One more GREAT video, really don't know what to say man, thank you :-)
Thanks for the effort!
really great explaination thanks!
Dude, u are a perfect teacher
what a great video, thank you very much
Thanks for the great video! One question Sir, do you also have a video of mean square error decomposing to Bias + Variance? I am confused in why some expectation are constant, and what the expectation over of. Thank you so much!
wow. perfectly explained. holy moly
man so good explained really.
Thank you for the video, great job! But I have a question that based on my understanding, variance here should be the difference between prediction accuracy from different test data set. Please correct me if I am wrong. Thanks again for the video, love it.
Fantastic explaination
Thank you very much, as always spot on
Brilliant explanation
Amazing explanation... tkx a lot
Sir great explanation, plz make videos on statistical inference
Excellent explanation - thank you for this video
Glad it was helpful!
Great video man! Tnx a bunch!
Glad it helped!
Excellent explanation
This man is amazing.
Well Explained!!
very clean explanation
I like how u explain it, can u make videos about LASSO, SCAD and MCP, I still confused about them..
I do have a LASSO video:
th-cam.com/video/jbwSCwoT51M/w-d-xo.html
And thanks for the other suggestions!
Well said! Keep up the good work.
Thanks, will do!
Really good explanation
Amazing video
Great presentation!
You mentioned that complex models have a tendency to pick up both real patterns and noise from the dataset they are trained on, so their predictions are all different (high variance) due to the noise, but correct on average.
I'm wondering, if you have a very large dataset, why isn't it viable to train the same very complex (strongly overfitted) model on N chunks of the dataset and use the average prediction of these N models?
love the marker flip at 5:39 LOL
wow you make it so easy to understand
Thanks!
Very good video. Thanks!
Glad you liked it!
Recommend future video suggestion: Fisher Information.
Thanks alot 👏👏
Genious!
So should we strive to optimize the _product_ of bias and variance?
thanks that helped a lot
No problem!
Great explanation
Glad it was helpful!
Thank you so much]
Thank you!
Of course!
Few variables, consistent prediction across datasets, average prediction does not get closer to true value. Many variables, inconsistent prediction across datasets (as predictor function models the idiosyncratic noise in the data - leading to high variance in predictions or prediction decisions) , average prediction gets closer to true value (as noise from individual datasets cancel each other - leading to low bias or error between the average across predictions and the true value).
Thanks!!
thank you
Perfect
Awesome
Being a researcher, I should say that when you defined the term bias, you should also explain what is variance else this isn’t for dummies.