Polynomial regression

[@BrandonSLockey]

BIC at 7:05 great explanation, insanely good

[@drewfrench8784]

Just found your content and I find it far clearer than most other creators who try to explain similar concepts. Good job

[@mikexcohen1]

Thank you kindly, Drew.

[@nzrinliyeva2913]

How can I define polynomial regression coefficient, which method can I use?

[@sudiptochakrabarti9469]

Please refer the video, where you taught how to calculate SSE for specific K in the BIC formula

[@ahmadasadillah8392]

Hey great vid! Where can I watch your video about the sum of squares of the residuals?

[@jotkej9748]

Looking for that too

[@russellkemmit73]

Udemy Mike Cohen, Machine Learning Course

[@ahmadasadillah8392]

Thank you

[@bit-colombo5595]

Hello is there a video of implementing polynomial regression in python

[@mikexcohen1]

Not in this video, but in the full course, yes, there are examples in Python code with explanations.

[@OsmanGani-b6j]

Why did not you say about decent gradient algorithm? Is BIC the alternative algorithm?

[@mikexcohen1]

Gradient descent is a nonlinear search algorithm. It's very powerful for large, complex problems that have no closed-form solution. But regression has a closed-form solution (least squares) that can be mathematically proven to be optimal.

[@maryammaryamian2748]

What is your nulhypothesis and alternative hypothesis when you have a polynimial term in your regression when we are doing a t-test for each variable? Imagine you have y = b1 + b2 age + b3 age ^2? And you think that age has a negative effect on y over time?

[@mikexcohen1]

The null hypothesis of regressors in a model is always the same: That the coefficient (the beta value) is statistically indistinguishable from zero.

[@MrCentrax]

I can't find the video about the SSe formula

[@valovanonym]

It's actually the mse

[@Evan490BC]

Bayes information criterioN. Criteria is plural.

[@erickjian7025]

shouldn't natural log be ln? I though log is base 10 log

[@mikexcohen1]

Yikes! Yes, you're correct, and it's a bit of a typo there. I guess I was mixing code and math while writing out that equation. Anyway, my apologies for the confusion, and good catch!

[@erickjian7025]

@@mikexcohen1 All good :) like your video !!!

[@n8trh]

If memory serves, in some math books, e is the default for a log base instead of 10, i.e. "log" is used for "ln".

[@kanamarlapudimukhesh5619]

At 7:45 what is formula for SS?

[@navalkumarshukla9447]

I think it's MSE only

[@pritamroy3766]

hi mike, I have a question, that is, lets say i have data points, now I made a curve a manually (lets imagine it is possible ) joining each points one by one. it will give a some crooked curve obviously, now if I calculate BIC for this crooked curve will it give minimize value ?

[@jayZander]

why is a sub 2 equal to 0?

[@sofiansammar335]

WOW, amazing thank you, the best video about Polynomial regression.

[@mikexcohen1]

Thank you, kind internet stranger.

[@codecaine]

Excellent content

[@mikexcohen1]

Glad you enjoyed it :)

[@renaldomoon3097]

Nice and short explanation, thanks my dude!

[@mikexcohen1]

You got it, bro

[@philippu1455]

Mike! I really enjoyed this insightful video. What method do you regard as the best when incorporating noise for polynomial functions?

[@mikexcohen1]

Hi Philipp. There are many ways to simulate noise, depending on the goal. The easiest thing to do is to use white noise (randn in MATLAB or np.random.randn in Python) with a suitable standard deviation.

[@philippu1455]

​@@mikexcohen1 Thank you for the reply. What I am looking at is a neural network that approximates a polynomial given x values as input and their associated function values (y) as the label. I want to test the robustness of the model by adding noise to the function values of the training data. Would you say that the use of a normal distribution with a suitable standard deviation and the original, "true" y as the mean is appropriate for this endeavor?

[@judahdsouza9196]

@@philippu1455 yeah that should work

[@oneness-divinebeings1115]

Very nice explanation.. Thank you so much

[@MB-rt8so]

Thank you for this video, pls give examples for calculating best fit no. of degree & sample size calculation for polynomial equation, As per Bayes (BIC) equation.

[@gvbvwockee]

Really helpful stuff! Thank you.

[@mo_l9993]

This video helped me a lot, thank you!

[@tenzinis5572]

My first video, already hooked!

[@mikexcohen1]

Welcome to the team ;)

[@siddhft3001]

Great explanation. Thank you!

[@vgreddysaragada]

well narrated..Thank you

[@sivakumar-ho3mw]

Appreciated Man !! Great Job

[@Thriver21]

Tq

[@swghavoc]

Humm

[@vikasjaswal9416]

you are awesome

[@mikexcohen1]

No, you're awesome! ... well, let's both be awesome ;)