Great video and helpful channel! Khan academy and the organic chemistry guy are getting old and less helpful as school curriculums develop. Super grateful for these simple, direct explanations
@@Rashidiillactually it's the other way around. It's better to use absolute value instead of squares as it can amplify the outliers and influence the final fit.
think like you are removing a base line function from the data points, either linear (like pca, f(x)=kx+c) or polynome (nonlinear pca, f(x)=...+ax^2+bx+c), then checking if the noise is from a normal distribution, ie, trying to make the noise after removing the base line as normal as possible, if you do linear, the noise might not be normal, so you get only a partial pca component fit, kinda
Visual learning makes things so much better.
excellent animation and explanation, simple to follow and understand!
Omg, your explanation is better than other youtube videos and my teacher because I'm a visual learner.
Noice
compact and thorough at the same time. thanks !
Great video and helpful channel! Khan academy and the organic chemistry guy are getting old and less helpful as school curriculums develop. Super grateful for these simple, direct explanations
Simple yet extremely informative👍
Thanks a lot, I have the curse of being a visual learner and this was amazing.
Excellent video and also quite easy to understand
lovely brooo, such good animation, now i have the concept in my head.
This is a great little video !
clear and brief idea
Thank you for simplifying this
All videos are excellent
Just perfect. Thanks
Excellent! Thank you.
great explanation!
Thank you... ❤
in 2 mins just you explained everything
Why use the squares instead of the absolute values?
because they are easier to compute and deal with mathematically. But we can use absolute values too!
because it gives more clear picture if we have error of ,1 and if we square it it will give 0,01 which is kind of scaled.
@@Rashidiillactually it's the other way around. It's better to use absolute value instead of squares as it can amplify the outliers and influence the final fit.
But residual != error?
fit a function f(x) to data with normal noise, f(x) can be a line, or a polynomial, etc, includes outlier handling, least squares is very sus
line with normal noise is a better answer than just a line
constant std additive normal noise assumed
think like you are removing a base line function from the data points, either linear (like pca, f(x)=kx+c) or polynome (nonlinear pca, f(x)=...+ax^2+bx+c), then checking if the noise is from a normal distribution, ie, trying to make the noise after removing the base line as normal as possible, if you do linear, the noise might not be normal, so you get only a partial pca component fit, kinda
no example, pretty useless