fs_lasso: Feature Selection using Lasso
In daltoolboxdp: Python-Based Extensions for Data Analytics Workflows
fs_lasso R Documentation
Feature Selection using Lasso
Description
Feature selection using Lasso regression is a technique for selecting a subset of relevant features.
It wraps the glmnet library.
Usage
fs_lasso(attribute)
Arguments
attribute
The target variable.
Value
A fs_lasso object.
Examples
data(iris)
myfeature <- daltoolbox::fit(fs_lasso("Species"), iris)
data <- daltoolbox::transform(myfeature, iris)
head(data)
daltoolboxdp documentation built on June 29, 2025, 5:07 p.m.
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| fs_lasso | R Documentation |
Feature Selection using Lasso
Description
Feature selection using Lasso regression is a technique for selecting a subset of relevant features. It wraps the glmnet library.
Usage
fs_lasso(attribute)
Arguments
attribute |
The target variable. |
Value
A fs_lasso object.
Examples
data(iris)
myfeature <- daltoolbox::fit(fs_lasso("Species"), iris)
data <- daltoolbox::transform(myfeature, iris)
head(data)
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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