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Using `Blended Conditional Gradient (BCP)` as node solver produces non integral vertices for some instances of the sparse regression, portfolio and lasso problem.
The issue seems to stem from the …
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The simulated data's ground truth is mostly zeros, with only a few non-zeros. Random lasso's is outputting weights with all non-zeros. The weights outputting are semi-reasonable, meaning the higher va…
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The `penalty` argument in `linear_reg()` and `logistic_reg()` only applies to `glmnet`, `keras` and `spark`. I think this could be added to `stan` with a slight change. This code shows the setup for `…
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This isnt an issue per se. I did want to figure out if I could use a similar approach for a simple LASSO regression in pytorch. Working with proximal operators with SGD is straightforward (but then SG…
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I have a large matrix (150000 by 32000) I am trying to run a linear SVC using scikit-learn to perform a lasso regression
``` lsvc = LinearSVC(C=0.01, penalty="l1", dual=False).fit(df, y) ```
I …
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try ridge regression because G/xG/g/a1/a2 are too highly correlated.
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Hello,
I am trying to use OEM for grouped LASSO on a tall dataset which contains many categorical variables (and as a result, lots of binary variables in the model matrix). While running cv.oem, I k…
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We comment saying that lightning always fits an intercept:
https://github.com/benchopt/benchmark_lasso/blob/main/solvers/lightning.py#L10
and indeed it has an attribute `intercept_` after fitting.
…
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We have a rich set of examples covering a very broad range of issues, but they're not necessarily easily discoverable via the section on the bottom of the API pages.
This meta-issue is to keep trac…
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I'm needing to run logistic regression, ideally with the ability to be multinomial. I noticed that you ( @simonster ) also built a wrapper for GLMnet; have you had any plans on adding this feature to …