On Sat, Jul 28, 2012 at 3:13 PM, Zach Bastick wrote:
> The docs do not indicate whether there is anyway to do a stepwise
> regression in scikit-learn or in Python.
> All there seems to be is linear_model.LinearRegression().
>
> This function outputs resulting x-values/beta-values/coefficents that
> When running linear_model.Lars I get the following error.
>
> C:\Python27\lib\site-packages\sklearn\linear_model\least_angle.py:237:
> RuntimeWa
> rning: invalid value encountered in divide
>z = -coefs[n_iter, active] / least_squares
should be fixed in master:
https://github.com/scikit-lear
When running linear_model.Lars I get the following error.
C:\Python27\lib\site-packages\sklearn\linear_model\least_angle.py:237:
RuntimeWa
rning: invalid value encountered in divide
z = -coefs[n_iter, active] / least_squares
The script still runs, and I get coefficents and an intercept.
Als
you can also look at
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lars.html#sklearn.linear_model.Lars
which, as stepwise regression, cannot discard features as soon as they
are selected.
Alex
On Sun, Jul 29, 2012 at 12:32 AM, Olivier Grisel
wrote:
> 2012/7/28 Zach Bast
2012/7/28 Zach Bastick :
> The docs do not indicate whether there is anyway to do a stepwise
> regression in scikit-learn or in Python.
> All there seems to be is linear_model.LinearRegression().
>
> This function outputs resulting x-values/beta-values/coefficents that
> are not significant. In sta
The docs do not indicate whether there is anyway to do a stepwise
regression in scikit-learn or in Python.
All there seems to be is linear_model.LinearRegression().
This function outputs resulting x-values/beta-values/coefficents that
are not significant. In statistical data packages, like SPSS,
