Bias-Variance Tradeoff : Data Science Basics

[@mathieutorchia178]

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.

[@thefuzedout]

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

[@thepresistence5935]

This is the only video, I learnt about bias-variance tradeoff

[@rudeprover]

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!

[@shubhamsharma-ne2ke]

"Obviously", this is the best explanation of bias and variance.

[@souravkundu1735]

i have been searching for this topic all over the internet... but i have always got very complex explanations... youyr explanation finally saved the day. Thanks !

[@stanlukash33]

And here I am - coming back to your videos even after finishing an ML course. Thank you

[@jannatulferdaush377]

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

[@DigitalOutlawed]

Man, Thanks so so so so much, you have no idea how much time you save us students.

[@subhankarghosh1233]

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.

[@ritvikmath]

Glad it was helpful!

[@liamtarr]

I really enjoyed this explanation. This is a must watch for anyone who wants to start working with machine learning.

[@ritvikmath]

Glad you enjoyed it!

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[@nicowww89]

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!

[@danspeed93]

One of the clearest explanations of this tradeoff I've seen so far, thanks!

[@ArunKumar-yb2jn]

A wealth of wisdom in a nugget!

[@AdityaSharma-do1ho]

Hey Ritvik, I like the way you try to build up the intuitive sense around maths rather than focusing on the theorems! Great work!

[@honeyBadger582]

One of the best DS tutors out there. Keep it up!

[@ritvikmath]

Glad you think so!

[@sarfrazjaved330]

Hey @ritvikmath, This man is genius. He explains the complex stuffs so simple. Hats off sir.

[@houyao2147]

very clear about the definitions of variance and bias. It tells sth. about many models, not one.

[@Boringpenguin]

Thank you so much. This is by far the clearest explanation I have ever come across on this topic!

[@mateoricucci1285]

This is the best youtube video I've ever seen. Thank you so much

[@ritvikmath]

Wow, thanks!

[@Moongchilove]

Appreciate the video. What a intuitive, straight-to-the point lecture in a perfect play time

[@ataulmorshed3964]

The wow part was the explanation on contribution of each model to learn the average 'signal' (true pattern) and 'noise' of a data.

[@kushagraagrawal7292]

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!

[@woowooNeedsFaith]

Subtitles of this video are rally nice. Without it I wouldn't known you speak Korean.

[@dharmharley6871]

Pleasantly surprised to see this good of content on youtube!

[@yakovsushenok]

Simply explained is best for intuition

[@rahulahuja1412]

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.

[@monazaizan947]

Wow!! Very well explained!! I appreciated your effort. It is not easy to put all these together perfectly! Thank you so much!

[@jayneabrown]

You are an absolutely incredible teacher!

[@jackvaughan7265]

Made this crystal clear. Thank you for this content

[@augustoc.romero1130]

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.

[@DrNoureddinSadawi]

Great explanation Ritvik, thank you!

[@HonestConsulting]

Hey bro. You make excellent videos. Keep up the good work.

[@ritvikmath]

Thank you so much 😀

[@amyliu2777]

This makes so much sense! Thank you for the awesome explanation.

[@harrywilkinson4826]

This was a fantastic explanation. Thanks for the clarity!

[@aniket1983]

Thank you so much....this was one of the best explanation of variance- bias...again Thank you so much for all your videos...Respect...

[@ramiscanyakar5078]

amazing video pure informations well done thanks ritvik

[@keremaksoy8007]

Dude, u are a perfect teacher

[@justin.c249]

Very well explained! Great work!

[@王十八-k6w]

Best explanation on this topic! Thank you!

[@navanarun]

Thanks for simplifying such a complex topic!

[@omarz8390]

excellent presentation for boas and variance. Thank you

[@arkophredy]

The explanation was super!!! Thanks for sharing this

[@mprawesh]

My course book definition was so confusing but you made it so clear. Thank You!

[@ritvikmath]

Of course!

[@cara1362]

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.

[@tymothylim6550]

Wonderful video! Never really understood these terms when studying until now :) Thanks a lot!

[@derekfun75]

wow. You are so good! You really help me understand the concept completely!

[@ritvikmath]

I'm so glad!

[@chuckyneoable]

Awesome explanation. Sincere thanks

[@xavecoding]

Amazing explanation... tkx a lot

[@ryansandan7897]

this is the best explanation!

[@sameerpatel4731]

Excellent explanation - thank you for this video

[@ritvikmath]

Glad it was helpful!

[@anjamisimovic9214]

you are just GREAT dude... you explained it sooooooo nicely i cant believe

[@AriaHarmony]

Thank you very much this's exactly what I needed.

[@r.walid2323]

Thank you, what a great explanation

[@ShakrinJahanMozumder]

Can't thank you more! You're amazing, man!

[@ritvikmath]

Happy to help!

[@GeoffryGifari]

So should we strive to optimize the _product_ of bias and variance?

[@andreyb7419]

Love such an easily explained complex stuff! Thx a lot!!!

[@mango-strawberry]

wow. perfectly explained. holy moly

[@estagidados302]

great explanation. thank you, master

[@DarkShadow-tm2dk]

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

[@soajack]

Great explanation ! Thanks !

[@ChocolateMilkCultLeader]

Fantastic explaination

[@699ashi]

Yes, I did intuitively understood the concept. Thanks

[@ariellise]

very clean explanation

[@jwdex]

Thank you so much! Great explanations, wish I would've watched this sooner

[@jakubjan44]

Really good explanation

[@MNCAMANI15]

This man is amazing.

[@mrbeancanman]

really great explaination thanks!

[@peterc.2301]

Absolutely Perfect!!!

[@Werdnabae]

love the marker flip at 5:39 LOL

[@hiteshthakur416]

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.

[@alejandrocanada2180]

what a great video, thank you very much

[@SPeeDKiLL45]

man so good explained really.

[@alexcessy]

Great explanation

[@ritvikmath]

Glad it was helpful!

[@zephyrsurfteam]

Well said! Keep up the good work.

[@ritvikmath]

Thanks, will do!

[@arrozenescau1539]

wow you make it so easy to understand

[@ritvikmath]

Thanks!

[@smilingbread22]

I like how u explain it, can u make videos about LASSO, SCAD and MCP, I still confused about them..

[@ritvikmath]

I do have a LASSO video: kzbin.info/www/bejne/oJPahHatpLloZ68 And thanks for the other suggestions!

[@mosama22]

One more GREAT video, really don't know what to say man, thank you :-)

[@ICEICISIC]

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).

[@leonhardolaye-felix8811]

Amazing video

[@canernm]

Very good video. Thanks!

[@ritvikmath]

Glad you liked it!

[@karimmohanad2k01]

Thanks for the effort!

[@user-or7ji5hv8y]

Recommend future video suggestion: Fisher Information.

[@prathammehta4290]

Well Explained!!

[@ebateru]

Great video man! Tnx a bunch!

[@ritvikmath]

Glad it helped!

[@sairabaig7272]

Sir great explanation, plz make videos on statistical inference

[@diegososa5280]

Thank you very much, as always spot on

[@P4ExHzLRuuiFMg3X4U3v]

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?

[@jierunchen873]

Nice tutorial. Suddenly at around the 8th min, the message behind the bias-variance just pop out!

[@ritvikmath]

Glad it helped!

[@janarthananpoornavel]

Thanks a lot ritvik

[@ritvikmath]

You're welcome 😊

[@zrinkaduvnjak8037]

Hi, I'm a pharmacist by training and at the moment doing a PhD in the field of pharmacometrics. I love your videos! It is just amazing that you are able to explain them to someone without a proper math or statistics background. In my field we are using quite a few data science concepts but in quite different (but at the same time similar) ways. I thought this video could be a great opportunity to share this with you (so you would keep explaining everything in such a simple way). In pharmacometrics, we are working with clinical trials data and developing nonlinear mixed-effects models to explain changes in concentrations of drugs in the human body over time (we have multiple drug concentration measurements over time, together with patient characteristics available). Our models are sets of ordinary differential equations and most of the parameters in these models are treated as random effects parameters (we can not pool all the samples from all the patients together, but need to account for samples belonging to the same individual, and that patients differ from one from another). Bias-variance tradeoff comes in place in the last stage of model development (after general trends were accounted for with the system of ODEs), when we are trying to find covariates on model parameters (some patient characteristics, such as age, or some blood measurement). In contrast to your field, we do not have multiple sample datasets available (splitting is not an option since the dataset size is rather small) and it usually takes years after we publish our models that somebody actually tries to validate them on external data. We opt for complex models that contain all possible covariates when we want to make inferences on covariate effects, or more precisely - when we want to prove that dose adjustment is not needed (when we want to estimate these negligible parameters), but we opt for a parsimonious model when we want to use it for simulations (other clinical trials, same special populations such as obese people etc.). Also when we are talking about bias in oversimplified models, I believe in the first place we mean that over parameter estimates (and not predictions) are biased, since if we do not include 2 covariates, but only 1, and the two are correlated, estimate of one will also account for not including the second one (estimated effects seems to be higher than it is). After watching your video I finally understand why we are saying that simpler models are more predictive. Thank you!

[@SergioPerez-wj1nz]

Great explanation. I hope you are a teacher somewhere.

[@GeoffryGifari]

High variance doesn't seem to bad... is the downside the lack of efficiency (computational cost too high for overly complex models)?

[@andrzejserafin2758]

thanks that helped a lot

[@ritvikmath]

No problem!

[@ramankutty1245]

Very well explained. I love your videos, though you speak really fast!

[@ritvikmath]

Thank you! 😃

[@baurks]

This is great. But what exactly is bias? I heard 2 different things, 1) it means how different the different models are in terms of their predictions, 2) how far their predictions are from their true values If I got it these right, definitely confusing. Could you please clarify? Thank you

[@yuckbutyup]

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.

[@AhmedKhaliet]

Thanks alot 👏👏

[@jordon603]

Hi, thanks for the video.

[@ritvikmath]

Thanks for watching!

[@reee896]

Awesome