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
Examples
data("credit_woe")
m <- glm(bad ~ ., family = binomial, data = credit_woe)
featsel_glmnet(m)
#> Training
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#>
#> Call: glm(formula = as.formula(formula), family = binomial(link = logit),
#> data = model$data)
#>
#> Coefficients:
#> (Intercept) age_woe flag_res_phone_woe
#> -1.4032 0.5962 0.6872
#> marital_status_woe months_in_the_job_woe area_code_res_phone_woe
#> 0.3832 0.5959 0.4890
#> sex_woe payment_day_woe profession_code_woe
#> 0.7772 0.8917 0.5234
#> id_client_woe
#> 1.0029
#>
#> Degrees of Freedom: 49693 Total (i.e. Null); 49684 Residual
#> Null Deviance: 49380
#> Residual Deviance: 46700 AIC: 46720