Just found your content and I find it far clearer than most other creators who try to explain similar concepts. Good job
@mikexcohen1
3 ай бұрын
Thank you kindly, Drew.
@BrandonSLockey
3 жыл бұрын
BIC at 7:05 great explanation, insanely good
@sudiptochakrabarti9469
9 ай бұрын
Please refer the video, where you taught how to calculate SSE for specific K in the BIC formula
@sofiansammar335
2 жыл бұрын
WOW, amazing thank you, the best video about Polynomial regression.
@mikexcohen1
2 жыл бұрын
Thank you, kind internet stranger.
@nzrinliyeva2913
3 жыл бұрын
How can I define polynomial regression coefficient, which method can I use?
@codecaine
Жыл бұрын
Excellent content
@mikexcohen1
Жыл бұрын
Glad you enjoyed it :)
@ahmadasadillah8392
3 жыл бұрын
Hey great vid! Where can I watch your video about the sum of squares of the residuals?
@jotkej9748
3 жыл бұрын
Looking for that too
@russellkemmit73
2 жыл бұрын
Udemy Mike Cohen, Machine Learning Course
@ahmadasadillah8392
2 жыл бұрын
Thank you
@pritamroy3766
3 жыл бұрын
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 ?
@renaldomoon3097
Жыл бұрын
Nice and short explanation, thanks my dude!
@mikexcohen1
Жыл бұрын
You got it, bro
@OsmanGani-b6j
10 күн бұрын
Why did not you say about decent gradient algorithm? Is BIC the alternative algorithm?
@mikexcohen1
8 күн бұрын
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.
@gvbvwockee
4 жыл бұрын
Really helpful stuff! Thank you.
@Evan490BC
3 жыл бұрын
Bayes information criterioN. Criteria is plural.
@bit-colombo5595
3 жыл бұрын
Hello is there a video of implementing polynomial regression in python
@mikexcohen1
3 жыл бұрын
Not in this video, but in the full course, yes, there are examples in Python code with explanations.
@MB-rt8so
3 жыл бұрын
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.
@maryammaryamian2748
3 жыл бұрын
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
3 жыл бұрын
The null hypothesis of regressors in a model is always the same: That the coefficient (the beta value) is statistically indistinguishable from zero.
@mo_l9993
2 жыл бұрын
This video helped me a lot, thank you!
@siddhft3001
3 жыл бұрын
Great explanation. Thank you!
@tenzinis5572
3 жыл бұрын
My first video, already hooked!
@mikexcohen1
3 жыл бұрын
Welcome to the team ;)
@erickjian7025
3 жыл бұрын
shouldn't natural log be ln? I though log is base 10 log
@mikexcohen1
3 жыл бұрын
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
3 жыл бұрын
@@mikexcohen1 All good :) like your video !!!
@n8trh
2 жыл бұрын
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".
@philippu1455
4 жыл бұрын
Mike! I really enjoyed this insightful video. What method do you regard as the best when incorporating noise for polynomial functions?
@mikexcohen1
4 жыл бұрын
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
3 жыл бұрын
@@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
2 жыл бұрын
@@philippu1455 yeah that should work
@sivakumar-ho3mw
4 жыл бұрын
Appreciated Man !! Great Job
@jayZander
3 жыл бұрын
why is a sub 2 equal to 0?
@MrCentrax
2 жыл бұрын
I can't find the video about the SSe formula
@valovanonym
2 жыл бұрын
It's actually the mse
@vikasjaswal9416
4 жыл бұрын
you are awesome
@mikexcohen1
4 жыл бұрын
No, you're awesome! ... well, let's both be awesome ;)
Пікірлер: 49