I want to create a 5-fold CV logistic regression model with lasso regularization, but I get this error message: Something is wrong; all the RMSE metric values are missing:
.
I started with logistic regression with lasso regularization by setting alpha=1
. This works. I expanded from this example.
# Load data set
data("mtcars")
# Prepare data set
x <- model.matrix(~.-1, data= mtcars[,-1])
mpg <- ifelse( mtcars$mpg < mean(mtcars$mpg), 0, 1)
y <- factor(mpg, labels = c('notEfficient', 'efficient'))
#find minimum coefficient
mod_cv <- cv.glmnet(x=x, y=y, family='binomial', alpha=1)
#logistic regression with lasso regularization
logistic_model <- glmnet(x, y, alpha=1, family = "binomial",
lambda = mod_cv$lambda.min)
I read that the glmnet
function already does 10-fold cv. But I want to use 5-fold cv. So when I add that modification using n_folds
to cv.glmnet
, I can't find the minimum coefficient nor can I just make the model when modifying trControl
.
#find minimum coefficient by adding 5-fold cv
mod_cv <- cv.glmnet(x=x, y=y, family='binomial', alpha=1, n_folds=5)
#Error in glmnet(x, y, weights = weights, offset = offset, #lambda = lambda, :
# unused argument (n_folds = 5)
#logistic regression with 5-fold cv
# define training control
train_control <- trainControl(method = "cv", number = 5)
# train the model with 5-fold cv
model <- train(x, y, trControl = train_control, method = "glm", family="binomial", alpha=1)
#Something is wrong; all the Accuracy metric values are missing:
# Accuracy Kappa
#Min. : NA Min. : NA
# 1st Qu.: NA 1st Qu.: NA
# Median : NA Median : NA
# Mean :NaN Mean :NaN
# 3rd Qu.: NA 3rd Qu.: NA
# Max. : NA Max. : NA
# NA's :1 NA's :1
Why does the error arise when I add 5-fold cv?
There are 2 problems in your code:
1) the n_folds
argument in cv.glmnet
is actually called nfolds
and 2) the train
function takes no alpha
argument. If you fix these your code works:
# Load data set
data("mtcars")
library(glmnet)
library(caret)
# Prepare data set
x <- model.matrix(~.-1, data= mtcars[,-1])
mpg <- ifelse( mtcars$mpg < mean(mtcars$mpg), 0, 1)
y <- factor(mpg, labels = c('notEfficient', 'efficient'))
#find minimum coefficient
mod_cv <- cv.glmnet(x=x, y=y, family='binomial', alpha=1)
#logistic regression with lasso regularization
logistic_model <- glmnet(x, y, alpha=1, family = "binomial",
lambda = mod_cv$lambda.min)
#find minimum coefficient by adding 5-fold cv
mod_cv <- cv.glmnet(x=x, y=y, family='binomial', alpha=1, nfolds=5)
#logistic regression with 5-fold cv
# define training control
train_control <- trainControl(method = "cv", number = 5)
# train the model with 5-fold cv
model <- train(x, y, trControl = train_control, method = "glm", family="binomial")
model$results
#> parameter Accuracy Kappa AccuracySD KappaSD
#>1 none 0.8742857 0.7362213 0.07450517 0.1644257