Multivariable regression attribute selection in python

Question

I'm a beginner to using statsmodels & I'm also open to using other Python based methods of solving my problem:

I have a data set with ~ 85 features some of which are highly correlated. When I run the OLS method I get a helpful 'strong multicollinearity problems' warning as I might expect.

I've previously run this data through Weka, which as part of the regression classifier has an eliminateColinearAttributes option.

How can I do the same thing - get the model to chose which attributes to use instead of having them all in the model? Thanks!

how many observations do you have and how many of your parameters have very large standard errors? — Josef, Feb 23 '14 at 22:20
I have 4000 observations. Most of the standard errors are larger than the coefficients. — BinderNet, Feb 24 '14 at 18:10

score 1 · Accepted Answer · edited May 23 '17 at 10:26

To run multivariate regression use scipy.stats.linregress. Check out this nice example which has a good explanation.

The eliminateColinearAttributes option in the software you've mentioned is just some algorithm implemented in this software to fight the problem. Here, you need to implement some iterative algorithm yourself based on elimination of one of highly correlated variables with the highest p-value (then run regression again and repeat until multicollinearity is not there).

There's no one and only way here, there are different techniques. It is also a good practice to choose manually from the set of highly correlated with each other set of variables which to omit that it also makes sense.

Thanks @Max, I was hoping that someone had already done the hard work. — BinderNet, Feb 23 '14 at 15:40

Multivariable regression attribute selection in python

1 Answers1