data_mediumhard: Benchmarks for medium-hard task
In jolars/gsoc18saga: Test results for SAGA project in GSoC 2018
Description
A benchmark of SAGA L1-regularized logistic regression in the
scikit-learn python package and the python version of
glmnet based
on subsets of the covtype, ijcnn1, a9a, mushrooms,
skin_nonskin, and phishing datasets.
Usage
1
Format
A data frame with 800 rows and 5 variables:
- dataset
dataset
- package
package, glmnet or saga
- time
runtime in seconds
- loss
value of objective function
jolars/gsoc18saga documentation built on May 25, 2019, 6:24 p.m.
R Package Documentation
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Description
A benchmark of SAGA L1-regularized logistic regression in the scikit-learn python package and the python version of glmnet based on subsets of the covtype, ijcnn1, a9a, mushrooms, skin_nonskin, and phishing datasets.
Usage
1 |
Format
A data frame with 800 rows and 5 variables:
- dataset
dataset
- package
package, glmnet or saga
- time
runtime in seconds
- loss
value of objective function
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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