Train a Decision Tree Model for Classification

Trains a single Decision Tree model using caret::train (via rpart method) for binary classification.

Usage

dt_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 cp (complexity parameter) (if TRUE) or use a fixed value (if FALSE).

cv_folds

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

Value

A caret::train object representing the trained Decision Tree 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
dt_model <- dt_dia(X_toy, y_toy)
print(dt_model)
#> CART 
#> 
#> 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     
#>   0.4166667  0.3   0.5666667
#> 
#> Tuning parameter 'cp' was held constant at a value of 0.01
# }