logistic_regression_train: L2-regularized Logistic Regression Training and Prediction

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

L2-regularized Logistic Regression Training and Prediction

Description

An implementation of L2-regularized logistic regression for two-class classification. Given labeled data, a model is trained and saved for future use; or, a pre-trained model can be used to classify new points.

Usage

logistic_regression_train(
  training,
  batch_size = 64,
  labels = NA,
  lambda = 0,
  max_iterations = 10000,
  optimizer = "lbfgs",
  print_training_accuracy = FALSE,
  step_size = 0.01,
  tolerance = 1e-10,
  verbose = getOption("mlpack.verbose", FALSE)
)

Arguments

training	A matrix containing the training set (the matrix of predictors, X) (numeric matrix).
batch_size	Batch size for SGD. Default value "64" (integer).
labels	A matrix containing labels (0 or 1) for the points in the training set (y) (integer row).
lambda	L2-regularization parameter for training. Default value "0" (numeric).
max_iterations	Maximum iterations for optimizer (0 indicates no limit). Default value "10000" (integer).
optimizer	Optimizer to use for training ('lbfgs' or 'sgd'). Default value "lbfgs" (character).
print_training_accuracy	If set, then the accuracy of the model on the training set will be printed (verbose must also be specified). Default value "FALSE" (logical).
step_size	Step size for SGD optimizer. Default value "0.01" (numeric).
tolerance	Convergence tolerance for optimizer. Default value "1e-10" (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).

Details

An implementation of L2-regularized logistic regression using either the L-BFGS optimizer or SGD (stochastic gradient descent). This solves the regression problem

y = (1 / 1 + e^-(X * b)).

In this setting, y corresponds to class labels and X corresponds to data.

This implementation can train a logistic regression model given training data (specified with the "training" parameter). A trained logistic regression model can then be used to perform classification on a test dataset (specified with the "test" parameter). Alternatively, classification probabilities can be computed and saved with the "probabilities" parameter.

The training data, if specified, may have class labels as its last dimension. Alternately, the "labels" parameter may be used to specify a separate matrix of labels.

When a model is being trained, there are many options. L2 regularization (to prevent overfitting) can be specified with the "lambda" option, and the optimizer used to train the model can be specified with the "optimizer" parameter. Available options are 'sgd' (stochastic gradient descent) and 'lbfgs' (the L-BFGS optimizer). There are also various parameters for the optimizer; the "max_iterations" parameter specifies the maximum number of allowed iterations, and the "tolerance" parameter specifies the tolerance for convergence. For the SGD optimizer, the "step_size" parameter controls the step size taken at each iteration by the optimizer. The batch size for SGD is controlled with the "batch_size" parameter. If the objective function for your data is oscillating between Inf and 0, the step size is probably too large. There are more parameters for the optimizers, but the C++ interface must be used to access these.

For SGD, an iteration refers to a single point. So to take a single pass over the dataset with SGD, "max_iterations" should be set to the number of points in the dataset.

This implementation of logistic regression does not support the general multi-class case but instead only the two-class case. Any labels must be either 0 or 1. For more classes, see the softmax regression implementation.

Value

A list with several components defining the class attributes:

output_model

Output for trained logistic regression model (LogisticRegression).

Author(s)

mlpack developers

Examples

# 
# #' # \dontrun{
# suppressMessages(library(mlpack)) # in case 'mlpack' is not yet loaded
# X <- as.matrix(read.csv("http://datasets.mlpack.org/iris.csv",
# header=FALSE))
# y <- as.matrix(read.csv("http://datasets.mlpack.org/iris_labels.csv",
# header=FALSE))
# pp <- preprocess_split(input=X, input_label=as.matrix(1:nrow(X)),
# test_ratio=0.2)
# X_train <- pp[["training"]]
# X_test <- pp[["test"]]
# # labels are indices to operate on both factors or numeric data
# y_train <- y[as.integer(pp[["training_labels"]]), 1]
# y_test <- y[as.integer(pp[["test_labels"]]), 1]
# 
# model <- logistic_regression_train(training=X_train, labels=y_train,
#   lambda=0.1)
#   }

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