featsel_glmnet: Feature selection via glmnet

In jbkunst/risk3r: Tools for credit risk modelling

Feature selection via glmnet

Description

This is a wrapper for 'glmnet::cv.glmnet' and return A 'glm' object using the selected variables from 'glmnet::cv.glmnet'.

Usage

featsel_glmnet(
  model,
  S = "lambda.1se",
  plot = TRUE,
  seed = 123,
  trace.it = 1,
  type.measue = "auc",
  ...
)

Arguments

model	model
S	From https://glmnet.stanford.edu/articles/glmnet.html#quick-start: 'lambda.min' is the value of λ that gives minimum mean cross-validated error, while 'lambda.1se' is the value of λ that gives the most regularized model such that the cross-validated error is within one standard error of the minimum.
plot	plot
seed	seed
trace.it	trace.it p
type.measue	type.measue
...	Additional argumentos for glmnet::cv.glmnet

Value

A ‘glm' object using the selected variables from 'glmnet::cv.glmnet'.#’

Examples

data("credit_woe")

m <- glm(bad ~ ., family = binomial, data = credit_woe)

featsel_glmnet(m)

jbkunst/risk3r documentation built on March 19, 2024, 10:49 p.m.