logistic_regression_classify: L2-regularized Logistic Regression Classification

In mlpack: 'Rcpp' Integration for the 'mlpack' Library

L2-regularized Logistic Regression Classification

Description

An implementation of L2-regularized logistic regression for two-class classification. Uses a trained model to classify new points.

Usage

## S3 method for class 'mlpack_logistic_regression'
predict(object, newdata, type = c("predictions", "probabilities"), ...)

logistic_regression_classify(
  input_model,
  test,
  decision_boundary = 0.5,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

object	An instantiated model object for which prediction is desired
newdata	A test data set
type	A character value selection predictions or probabilities
...	Additional optional arguments affecting the prediction
input_model	Existing model (parameters) (LogisticRegression).
test	Matrix containing test dataset (numeric matrix).
decision_boundary	Decision boundary for prediction; if the logistic function for a point is less than the boundary, the class is taken to be 0; otherwise, the class is 1. Default value "0.5" (numeric).
verbose	Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical).

Value

A list with several components defining the class attributes:

predictions

If test data is specified, this matrix is where the predictions for the test set will be saved (integer row).

Author(s)

mlpack developers

Examples

# \dontrun{ pred <- predict(model, newdata=X_test) }

mlpack documentation built on July 23, 2026, 1:08 a.m.