Train an Elastic Net (L1 and L2 Regularized Logistic Regression) Model for Classification

Trains an Elastic Net-regularized logistic regression model using caret::train (via glmnet method) for binary classification.

Usage

en_dia(X, y, tune = FALSE, cv_folds = 5)

Arguments

X

A data frame of features.

y

A factor vector of class labels.

tune

Logical, whether to perform hyperparameter tuning for lambda (if TRUE) or use a fixed value (if FALSE). alpha is fixed at 0.5 for Elastic Net.

cv_folds

An integer, the number of cross-validation folds for caret.

Value

A caret::train object representing the trained Elastic Net model.

Examples

# \donttest{
set.seed(42)
n_obs <- 50
X_toy <- data.frame(
  FeatureA = rnorm(n_obs),
  FeatureB = runif(n_obs, 0, 100)
)
y_toy <- factor(sample(c("Control", "Case"), n_obs, replace = TRUE),
                levels = c("Control", "Case"))

# Train the model
en_model <- en_dia(X_toy, y_toy)
print(en_model)
#> glmnet 
#> 
#> 50 samples
#>  2 predictor
#>  2 classes: 'Control', 'Case' 
#> 
#> No pre-processing
#> Resampling: Cross-Validated (5 fold) 
#> Summary of sample sizes: 40, 40, 40, 40, 40 
#> Resampling results:
#> 
#>   ROC    Sens  Spec
#>   0.475  0.1   0.9 
#> 
#> Tuning parameter 'alpha' was held constant at a value of 0.5
#> Tuning
#>  parameter 'lambda' was held constant at a value of 0.01
# }