xgb_model: Train a Gradient Boosting Model using XGBoost

In quickSentiment: A Fast and Flexible Pipeline for Text Classification

Train a Gradient Boosting Model using XGBoost

Description

This function trains a model using the xgboost package. It is highly efficient and natively supports sparse matrices, making it ideal for text data. It automatically handles both binary and multi-class classification problems.

Usage

xgb_model(
  train_vectorized,
  Y,
  test_vectorized,
  parallel = FALSE,
  tune = FALSE,
  weights = NULL
)

Arguments

train_vectorized	The training feature matrix (e.g., a 'dfm' from quanteda).
Y	The response variable for the training set. Should be a factor.
test_vectorized	The test feature matrix, which must have the same features as 'train_vectorized'.
parallel	Logical
tune	Logical
weights	A numeric vector of observation weights. Default is NULL

Value

A list containing four elements:

pred	A vector of class predictions for the test set.
probs	A matrix of predicted probabilities.
model	The final, trained 'xgb.Booster' model object.
best_lambda	Placeholder (NULL) for pipeline consistency.

Examples

## Not run: 
# Create dummy vectorized training and test data
train_matrix <- matrix(runif(100), nrow = 10, ncol = 10)
test_matrix <- matrix(runif(50), nrow = 5, ncol = 10)

# Provide column names (vocabulary) required by xgboost
colnames(train_matrix) <- paste0("word", 1:10)
colnames(test_matrix) <- paste0("word", 1:10)

y_train <- factor(sample(c("P", "N"), 10, replace = TRUE))

# Run xgboost model
model_results <- xgb_model(train_matrix, y_train, test_matrix)

## End(Not run)

quickSentiment documentation built on Aug. 29, 2026, 1:07 a.m.