# Machine Learning Fundamentals: Bias and Variance

## 36 thoughts on “Machine Learning Fundamentals: Bias and Variance”

1. Maulik Naik says:

Also, can you tube customize the like button to BAM!! that would be Great.. π

2. Maulik Naik says:

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).

3. George Carvalho says:

niceeeeee

4. GateCrashers says:

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

5. Santhosh Murali says:

wow! you are the best!

6. Finn Janson says:

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

7. Otonium says:

Double Bam…. so well narrated!

8. Lavneet Sharma says:

BAM!!!! I finally understood the idea

9. Marcus Cactus says:

Very instructive.

10. Bharath Shashanka Katkam says:

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

11. Divinity says:

Awesome thanks

12. Stefanos Moungkoulis says:

BAM. Subscribed.

13. yogurt1989 says:

Opens StatQuest Videos -> Automatically clicks 'Like'

14. Abeer B says:

awsome and very clear explanation!

15. Osama Hafez says:

Awesome explanation!

17. Bhavya Gandhi says:

He sounds like bert from the big bang theory ππ

18. P Flo says:

Faakin gr8 m8!

19. Ved Patel says:

They are squared so that it can be minimized( or differentiated) ant not for the reason you mentioned at 3:14

20. Arjun Kalidas says:

You are the male version of Phoebe Buffay!!! π

21. Banshidhar Kumar says:

Really good explanation!!

22. fet1 says:

3:25
KEVIN
We follow the usual pattern which is to
(i) import the class
(ii) instantiate the model and
(iii) fit the model with the training data.
(iv) Then we'll make our predictions by passing the entire Feature Matrix (X) to predict method of the fitted model and
(v) print out those predictions. Let's store those predictions in an object called y_pred.
>>>>>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<
(i) from sklearn.linear_model import LogisticRegression
(ii) logreg = LogisticRegression()
(iii) logreg.fit(X, y)
(iv) logreg.predict(X)
(v) y_pred = logreg.predict(X) (3:47)

23. εΊ·ζ΅ζ΅ says:

very refreshing video!! it was very helpful!!!

24. Ruoyu Sun says:

I think the 'variance error' here is simply caused by data noise, and it is equal for both high-variance models and high-biased models. Anyone has the same feeling?

25. David Carroll says:

I liked this video, dear algorithms please show it to others.

26. Shahruq Sarfaraz says:

My new favourite channel to learn the fundamentals of ML. Plus you use R!!! π₯

27. Caballerobot says:

BAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAM

28. Andy Low says:

isn't it stupid to have learning and test sets instead of one bigger learning set?

29. D T says:

Hit the like for your intro, yet to watch the session π

30. Asish Kumar Bhattacharjee says:

Why we take the square of the errors instead of taking their absolute values?

Great video , easy to understand , Thanks

32. Shamsuddeen Hassan says:

From Intro to Statistical Learning with Application in R. I fully grasp the picture of Bias and Variance. In addition, flexible techniques vs less flexible techniques now cement into my memory, before I just crammed the terminology without knowing exactly what it means. I will be a constant goer to this channel

33. Lizzy says:

You may have saved 10% of my bioinformatic's exam tomorrow.

34. Sabah Pirani says:

Thanks π

35. Anal fabrics says:

Josh you're the best

36. Channa G says:

love love!