logit_model: Train a Regularized Logistic Regression Model using glmnet

In quickSentiment: A Fast and Flexible Pipeline for Text Classification

Train a Regularized Logistic Regression Model using glmnet

Description

This function trains a logistic regression model using Lasso regularization via the glmnet package. It uses cross-validation to automatically find the optimal regularization strength (lambda).

Usage

logit_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). This should be a sparse matrix.
Y	The response variable for the training set. Should be a factor for classification.
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 two elements:

pred	A vector of class predictions for the test set.
probs	A matrix of predicted probabilities.
model	The final, trained 'cv.glmnet' model object.
best_lambda	The optimal lambda value found during cross-validation.

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 glmnet
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 logistic regression model (glmnet)
model_results <- logit_model(train_matrix, y_train, test_matrix)

## End(Not run)

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