fitrlinear for large data set

I am trying a large regression/lasso model with n=90000 rows and p=500 columns

[mhat,FitInfo]=fitrlinear(X,y,'Learner','leastsquares');

I tryied also additional parameters

'solve','sparsa'

'Regularization','lasso'

The problem is that, when X has 200 columns or more, all the elements of mhat.Beta are ZERO

Do you have any suggestion about that?

Thanks,

Alessandro

[Alessandro Fassò]

Note that rank(X)>200

Answer 1

[Aditya Patil]

With high dimensional data, it is expected that some of the predictors won't have much effect on the response.

As a workaround, you can try to reduce the dimension using Dimensionality Reduction and Feature Extraction techniques.

[Alessandro Fassò]

Thanks for your answer!

I agree that "some of the predictors won't have much effect ...", but I expect that others do have an effect (I know from preliminary correlation analysis and maller regression excercises).

Note that X has rank > 200.

The problem is that fitrlinear give me ALL the betas=0. It comes very fast despite the large dimension problem.

Of course one can perform some preliminary dimensionality reduction, but I expect this is made by the lasso option of fitrlinear, I tried in various exercises like

>> fitrlinear(..., 'regularization','lasso','lambda',lambda);

for various lambda.

[Aditya Patil]

Can you provide the data so that I can reproduce the issue? Also provide the output of the version command.