Model request: liblinear linear SVM

[zachmayer]

I expect it to scale better than kernlab and e1071.:

https://cran.r-project.org/web/packages/LiblineaR/LiblineaR.pdf

We can probably just implement the svm classifier / regressor, as glmnet already does a great job for non-svm linear models.

[dashaub]

@zachmayer The "RSofia" package also has a fast implementation. It no longer appears maintained, however.

[zachmayer]

@dashaub Good to know!

[dselivanov]

From my experience on large datasets LiblineaR significantly faster than glmnet. But when I tried it, it was a little bit buggy - destroyed sparsity , etc.

[topepo]

Any particular model(s) that you are interested in? From ?LiblineaR:

for multi-class classification
• 0 – L2-regularized logistic regression (primal)
• 1 – L2-regularized L2-loss support vector classification (dual)
• 2 – L2-regularized L2-loss support vector classification (primal)
• 3 – L2-regularized L1-loss support vector classification (dual)
• 4 – support vector classification by Crammer and Singer
• 5 – L1-regularized L2-loss support vector classification
• 6 – L1-regularized logistic regression
• 7 – L2-regularized logistic regression (dual)
for regression
• 11 – L2-regularized L2-loss support vector regression (primal)
• 12 – L2-regularized L2-loss support vector regression (dual)
• 13 – L2-regularized L1-loss support vector regression (dual)

[zachmayer]

for multi-class classification:
• 1 – L2-regularized L2-loss support vector classification (dual)
• 2 – L2-regularized L2-loss support vector classification (primal)
• 3 – L2-regularized L1-loss support vector classification (dual)

for regression
• 11 – L2-regularized L2-loss support vector regression (primal)
• 12 – L2-regularized L2-loss support vector regression (dual)
• 13 – L2-regularized L1-loss support vector regression (dual)

[topepo]

I just checked in the models for the dual formulation. Would that be sufficient?

[zachmayer]

Yup!

[topepo]

on CRAN