Bias and Variance are two fundamental concepts for Machine Learning, and their intuition is just a little different from what you might have learned in your statistics class. Here I go through two examples that make these concepts super easy to understand.

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17 Set 2018

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Vikash Chauhan 22 horas atrás

Such a simple and elegant explanation, thank you so much ....

StatQuest with Josh Starmer 15 horas atrás

Thank you! :)

Vivek Menon 2 dias atrás

Another great video. Thank you!!!! You couldn't have made it any simpler.

StatQuest with Josh Starmer 2 dias atrás

Thanks! :)

Long Nguyen-Vu 2 dias atrás

Clear and concise, extremely helpful for beginners before getting into the scary math behind

StatQuest with Josh Starmer 2 dias atrás

Thank you! :)

Rushi Raiyani 4 dias atrás

You, sir, are a legend!

StatQuest with Josh Starmer 4 dias atrás

Thanks! :)

Allan Kálnay 5 dias atrás

3:10: "they are squared so that negative distances do not cancel out the positive distances" -> why do we always do the squared errors instead of taking absolute values of the errors? These will be always positive as well. What's the difference, please?

Allan Kálnay 4 dias atrás

@StatQuest with Josh Starmer Okaay, I get it now! Thank you very much. Anyways, your videos helped me a lot with statistics already before and now again :)

StatQuest with Josh Starmer 5 dias atrás

The short answer is that it makes the math easier in the long run. Here's the longer answer: Often in machine learning and in statistics we want to minimize the distance from the a predicted value and the actual/observed value. Like at 3:10 - we have measured the distance between the line and the data. If we want to find the straight line that minimizes those distances (not a squiggly line that fits them perfectly, but a straight line that's not perfect) then the easiest way to do that is to take the derivatives of the differences between the observed values and the values predicted by the line. Anyway, taking the derivatives of squared values is way easier than taking the derivatives absolute values, because absolute values are not continuous at 0, and thus, the derivative of an absolute value does not exit at 0.

Jorge Leandro 6 dias atrás

Man, you're very didactic! For each statement, there is a 'because', so that your students never ends with a question mark in the head. Besides that, you don't mind to repeat the because's again and again in different ways, and that's what make things clearer. Why can't teachers, coaches, tutors realize that? Triple BAMMM!

StatQuest with Josh Starmer 6 dias atrás

Thank you very much!! :)

Ishita Saxena 9 dias atrás

thanks again my savior

StatQuest with Josh Starmer 8 dias atrás

Hooray! :)

sakunoful1 9 dias atrás

This is so very helpful but I can't help but cringe when he sings in the beginning or when he says bam x)

StatQuest with Josh Starmer 9 dias atrás

You can always skip over the intro. And the "bams" always follow something awesome. So when something awesome happens... skip the next 3 seconds. ;)

ZHIYUAN YAO 12 dias atrás

I cannot believe this channel only has 141K subs. Thank you for the great tutorials!

StatQuest with Josh Starmer 12 dias atrás

Thanks! :)

Timo Bohnstedt 15 dias atrás

I LOVE your videos!

StatQuest with Josh Starmer 14 dias atrás

Thank you! :)

Joel John J 19 dias atrás

Good Work 😀

StatQuest with Josh Starmer 19 dias atrás

Thanks! :)

queenforever 20 dias atrás

I went from BUMMED to DOUBLE BAM in six and a half minutes. God bless you!

StatQuest with Josh Starmer 19 dias atrás

Hooray! :)

Shivam Maheshwari 22 dias atrás

*Give this guy a medal*

StatQuest with Josh Starmer 22 dias atrás

Thanks!

yuthpati rathi Mês atrás

Amazing explanation

StatQuest with Josh Starmer Mês atrás

Thank you! :)

A Jawad Mês atrás

linear regression (aka least square) finally, now I can die in peace. you explain things in very nice way.

StatQuest with Josh Starmer Mês atrás

Thanks!

hari20001 Mês atrás

Brilliant and clear and concise explanation: the best i have seen!!! Congrats and many thanks.

StatQuest with Josh Starmer Mês atrás

Thank you! :)

Payton Zhong Mês atrás

what an interesting nerd Lmaooo

Yusra Shaikh Mês atrás

MAN!!! i was reading about bias and variance trade off, but not a word got into my head...this video made it beyond clear!! thanks a ton!!

StatQuest with Josh Starmer Mês atrás

Hooray! I'm glad the video was helpful. :)

srikar goud Mês atrás

3:09 psst. I can listen to this all day.

StatQuest with Josh Starmer Mês atrás

:)

SAMAR KHAN Mês atrás

Thank u soo much . Really liked the way u explained . I learnt n I njoyed it too. Plz make more videos like this on related topics.👍🏼👍🏼👍🏼👍🏼👍🏼

StatQuest with Josh Starmer Mês atrás

Thank you! Yes, I plan on making as many videos as possible.

João Pedro Voga Mês atrás

Amazing video, helped me a lot!

StatQuest with Josh Starmer Mês atrás

Awesome!

ati safarkhah Mês atrás

PERFECT AND CLEAR!

StatQuest with Josh Starmer Mês atrás

Awesome!

Chien-Hsun Lai Mês atrás

I like the way you say DOUBLE BAM!

StatQuest with Josh Starmer Mês atrás

Thank you! :)

sridevi A Mês atrás

Nice video. the concept was clearly explained with visualizations

StatQuest with Josh Starmer Mês atrás

Thank you!!! :)

Raman Jha 2 meses atrás

Man, you are amazing. I have listened to many so called self proclaimed educators who are nothing more than assholes. You are great !!!

StatQuest with Josh Starmer 2 meses atrás

Thanks! :)

Bhabesh Roy 2 meses atrás

Woah your original songs are beautiful too'

Bhabesh Roy 2 meses atrás

Awesome explaination

StatQuest with Josh Starmer 2 meses atrás

Thanks! :)

a a 2 meses atrás

I am a simple man: I see a StatQuest video - I give an upvote.

StatQuest with Josh Starmer 2 meses atrás

Nice! :)

Robert Smith 2 meses atrás

You should sell these videos as DVD sets. I bet a lot of educators would buy them.

Parijat Bandyopadhyay 2 meses atrás

Josh, Thanks for making such beautiful videos :) you rock man

StatQuest with Josh Starmer 2 meses atrás

@Pete Murphy Thanks! :)

Pete Murphy 2 meses atrás

Perfect statement, I absolutely agree, Josh is really a legend!!!!

StatQuest with Josh Starmer 2 meses atrás

Awesome! Thank you! :)

Sunny Dsouza 2 meses atrás

The BAMs are quite cringy

AdityaFingerstyle 2 meses atrás

At first yes but soon you'll crave to hear that. Double BAM !!

Tales Araujo 2 meses atrás

It's been a while I had sent a Portuguese translation to this video (b/c I really like your channel and I thought it was a great video to explain these "tricky" concepts - I wanted to show it to some awesome ppl who don't understand English properly). Why didn't you take it, though? =(

StatQuest with Josh Starmer 2 meses atrás

I am really, really sorry!!!! BRvid does not notify me when there is a translation, so I did not know that you did so much work. I just approved it (and about 15 other translations that I did not know about). Thank you so much for putting in the time and effort for the translation. I'm sorry it took so long for approval and from now on I will check for translations more frequently.

Daniel Rodrigues Pipa 2 meses atrás

The concepts of bias and variance are wrong. The correct are bias = E(\hat{\theta} - \theta) and var = E[(\hat{\theta} - \theta)^2]. They measure respectively how well the estimator performs in the mean (positives do cancel with negatives errors!) and how spread are the outcomes of the estimator.

Daniel Rodrigues Pipa 2 meses atrás

@StatQuest with Josh Starmer I checked the source and it's consistent with the statistical definition I gave, which is the only correct. Machine Learning is just statistical inference in disguise and, thus, is suppose to use statistical terminology consistently.

StatQuest with Josh Starmer 2 meses atrás

For more information, check out page 33 in the Introduction to Statistical Learning in R (which is a free download): faculty.marshall.usc.edu/gareth-james/ISL/

StatQuest with Josh Starmer 2 meses atrás

You're talking about bias and variance in terms of statistics. This video describes how the terms are used in machine learning. They are related, but different.

Joel Vaz 2 meses atrás

Thanks man, i do not know what the start was about, but your video really helped me. Thanks

Ahmed 2 meses atrás

Extremely Great

StatQuest with Josh Starmer 2 meses atrás

Thank you! :)

Dropfire Music 2 meses atrás

I have understood not only the Bias and Variance, but also even more ML terminology that has been quite difficult for me to understand until this point! Keep it up brother! Very good job :)

StatQuest with Josh Starmer 2 meses atrás

Awesome!!! :)

Nelson Tovar 3 meses atrás

You don't have idea of how huge help you just gave me. I'm currently working with some real data, and i'm kind of leaned towards a cuadratic model instead of an exponential one. Happens that this first fits with R = 0.96 and the second with R = 0.90, however the first model included some negative values and our response (Y) can't be negative. I was thinking in working with the absolute value of the cuadratic model however, i'm not sure if i should get to that extent to keep only some better adjustment, i mean, R=0.90 isn't bad either. I think this is the overfitting you just mentioned.

StatQuest with Josh Starmer 3 meses atrás

Awesome! Good luck with your models! :)

Raj Kiran Reddy Marri 3 meses atrás

Awesome and meticulous explanation , keep it up Josh !

StatQuest with Josh Starmer 3 meses atrás

Thank you! :)

Sepehr Alian 3 meses atrás

Thanks mate. That was great. Cheers

StatQuest with Josh Starmer 3 meses atrás

Thanks! :)

Tanishk Singh 3 meses atrás

Amazing explanation,you are Awesome!

StatQuest with Josh Starmer 3 meses atrás

Thank you! :)

superxp1412 3 meses atrás

Correct me if I'm wrong. At 2:56 the dot lines to show the distance to the line should be vertical toward the line.

superxp1412 3 meses atrás

@StatQuest with Josh Starmer Thanks for your reply. It makes sense.

StatQuest with Josh Starmer 3 meses atrás

@superxp1412 In machine learning (and Statistics), the distances from the data to the line that we use for prediction are measured by using a vertical line from the data and not a line perpendicular to the prediction line. The reason for this is that we want to use the values on the x-axis to predict values on the y-axis. Thus, a value on the x-axis corresponds to a value on the y-axis by way of the prediction line. If, instead, we measured the perpendicular distance between the data and the prediction line, then this relationship would be destroyed.

superxp1412 3 meses atrás

@StatQuest with Josh Starmer I mean the dotted lines on the left side should not be vertical. It should be perpendicular to the line to represent the distance between the dot and the line. Correct me If I'm wrong.

StatQuest with Josh Starmer 3 meses atrás

Ummmm. I'm not sure I understand your comment. The dotted lines on the left side are vertical. On the right side, the squiggly red dotted line fits the data perfectly, so the distance between it and the data is zero. Thus, there, are no black dotted lines to draw on the right side.

Anonymous Noman 3 meses atrás

We are not actually calculating the distance between predicted points and the straight line (which is actually vertical /perpendicular on the line) Instead, to find the error you have to just calculate abs( predicted y - original_y ) (in this case it's actually parallel to Y axis)

SunkuSai 3 meses atrás

Question: What kind of bear is best?

valor36az 3 meses atrás

I wish you will write a book.

StatQuest with Josh Starmer 3 meses atrás

One day when I have more time... :)

Vaibhav Bisht 3 meses atrás

This is some quality educational content...Keep up the good work brother!! Definitely gonna buy some merch to support the channel!!

StatQuest with Josh Starmer 3 meses atrás

Awesome! Thank you! :)

Rohit Pingale 3 meses atrás

The videos of statsquest are sweet spots!

StatQuest with Josh Starmer 3 meses atrás

Thanks!

genie52 3 meses atrás

Wow this was so straight to the point with great visuals that I managed to figure out all in one go! Great stuff!

StatQuest with Josh Starmer 3 meses atrás

Awesome!!! :)

Maulik Naik 3 meses atrás

Also, can you tube customize the like button to BAM!! that would be Great.. ;)

StatQuest with Josh Starmer 3 meses atrás

That would be awesome! :)

Maulik Naik 3 meses atrás

Have watched many of your videos and that have forced me to write a comment, Stat Quest is AWESOME!! and @Josh Starmer, I am you fan. The way you begin your videos and go about explaining some of the most difficult concepts in Statistics and Machine Learning is GREAT. Many books and tutorials mention making the complex simple, but rarely do so. This channel is not one of them, it truly makes things simple to understand. I have just one request (i think most of your followers would agree to this point), please write a book on Machine Learning and it's application of various algorithms (may be a series of books).

StatQuest with Josh Starmer 3 meses atrás

Thanks so much! If I ever have time, I'll write a book, but right now I only have time to do the videos.

George Carvalho 3 meses atrás

niceeeeee

GateCrashers 4 meses atrás

please explain what is the term bias in a linear regression formula ? please explain at simply as possible. thank you

Santhosh Murali 4 meses atrás

wow! you are the best!

StatQuest with Josh Starmer 3 meses atrás

Thanks! :)

Finn Janson 4 meses atrás

Thank goodness you exist... I've never ever understood why squaring the distances mattered until your foot note at 3:12

StatQuest with Josh Starmer 3 meses atrás

Nice! :)

Otonium 4 meses atrás

Double Bam.... so well narrated!

StatQuest with Josh Starmer 4 meses atrás

Thank you! :)

Lavneet Sharma 4 meses atrás

BAM!!!! I finally understood the idea

StatQuest with Josh Starmer 4 meses atrás

Hooray! :)

Marcus Cactus 4 meses atrás

Very instructive.

Bharath Shashanka Katkam 4 meses atrás

Thanks for the lovely explanation, Sir... Could we fit the squiggly line by using the Maximum Likelihood Estimation?

Bharath Shashanka Katkam 3 meses atrás

@Malini Aravindan Thank you, Ma'am. But, what if there is a squiggly line with a large sample size? In that context of large sample size, can't we go with the Maximum Likelihood Estimation, instead of RMLE?

Malini Aravindan 3 meses atrás

Use rmel - restricted mle

Divinity 4 meses atrás

Awesome thanks

Stefanos Moungkoulis 4 meses atrás

BAM. Subscribed.

yogurt1989 4 meses atrás

*Opens StatQuest Videos* -> Automatically clicks 'Like'

Abeer B 4 meses atrás

awsome and very clear explanation!

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