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
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
@@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.
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
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
Thanks a lot, I have the curse of being a visual learner and this was amazing.
compact and thorough at the same time. thanks !
Simple yet extremely informative👍
Very good, finally understand how that best fit line we get!!
lovely brooo, such good animation, now i have the concept in my head.
Excellent video and also quite easy to understand
This is a great little video !
Thank you for simplifying this
clear and brief idea
Just perfect. Thanks
Excellent! Thank you.
All videos are excellent
great explanation!
Thank you... ❤
in 2 mins just you explained everything
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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
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?
no example, pretty useless