A note please (to Sebastian Raschka, mrschots).
The OLS model that I used ( where the test score gave me a negative
value) was not a good fit. Initial findings showed that t*he
regression coefficients and the model as a whole were significant,*yet
, finally , it failed in two econom
Thanks to all of you for your kind response. Indeed, it is a
great learning experience. Yes, econometrics books too create models for
prediction, and programming really makes things better in a complex
world. My understanding is that machine learning does depend on
econometrics too.
My
The R2 function in scikit-learn works fine. A negative means that the
regression model fits the data worse than a horizontal line representing the
sample mean. E.g. you usually get that if you are overfitting the training set
a lot and then apply that model to the test set. The econometrics book
In the simplest case of a simple linear regression what you wrote holds
true: the explained variance is simply a sum of variance explained by the
model and the residual variability that cannot be explained, and that would
always lie between 0 and 1. e.g. here:
https://online.stat.psu.edu/stat500/le
There is no constraint, that’s the point since nothing limits you to have a
model with crap predictions leading to be worse than to just predict the
target’s mean for every data point.
If you do so —> negative R2.
Best Regards,
Em qui., 12 de ago. de 2021 às 16:21, Samir K Mahajan <
samirkmahaja
Dear Christophe Pallier, Reshama Saikh and Tromek Drabas,
Thank you for your kind response. Fair enough. I go with you R2 is not a
square. However, if you open any book of econometrics, it says R2 is a
ratio that lies between 0 and 1. *This is the constraint.* It measures
the proportion or
