fitrlinear for large data set

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Alessandro Fassò
Alessandro Fassò 2021-2-25
评论： Aditya Patil 2021-3-29
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

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回答（1 个）

Aditya Patil
Aditya Patil 2021-3-29
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.
  2 个评论
Alessandro Fassò
Alessandro Fassò 2021-3-29
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
Aditya Patil 2021-3-29
Can you provide the data so that I can reproduce the issue? Also provide the output of the version command.

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