quickSentiment (version 0.1.0)

logit_model: 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)

Value

A list containing two elements:

pred

A vector of class predictions for the test set.

model

The final, trained `cv.glmnet` model object.

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

Examples

# Create dummy vectorized data
train_matrix <- matrix(runif(100), nrow = 10)
test_matrix <- matrix(runif(50), nrow = 5)
y_train <- factor(sample(c("P", "N"), 10, replace = TRUE))

# Run model
model_results <- logit_model(train_matrix, y_train, test_matrix)
print(model_results$pred)