cla_glmnet: LASSO logistic regression (glmnet)

In daltoolbox: Leveraging Experiment Lines to Data Analytics

LASSO logistic regression (glmnet)

Description

Logistic regression with L1 penalty using glmnet::cv.glmnet.

Usage

cla_glmnet(attribute, lambda = c("lambda.min", "lambda.1se"))

Arguments

attribute	target attribute name (binary)
lambda	which lambda to use ("lambda.min" or "lambda.1se")

Value

returns a cla_glmnet object

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
 data(iris)
 iris_bin <- iris
 iris_bin$IsVersicolor <- factor(ifelse(
   iris_bin$Species == "versicolor",
   "versicolor",
   "not_versicolor"
 ))
 model <- cla_glmnet("IsVersicolor")
 model <- fit(model, iris_bin)
 pred <- predict(model, iris_bin)
 eval <- evaluate(model, adjust_class_label(iris_bin$IsVersicolor), pred)
 eval$metrics
}

daltoolbox documentation built on May 14, 2026, 9:06 a.m.