lars_predict: LARS Prediction

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

LARS Prediction

Description

An implementation of Least Angle Regression (stagewise/lasso), also known as LARS. This program can use a pre-trained LARS/LASSO/Elastic Net model to output regression predictions from a test set.

Usage

## S3 method for class 'mlpack_lars'
predict(object, newdata, ...)

lars_predict(input_model, test, verbose = getOption("mlpack.verbose", FALSE))

Arguments

object	An instantiated model object for which prediction is desired
newdata	A test data set
...	Additional optional arguments affecting the prediction
input_model	Trained LARS model to use (LARS).
test	Matrix containing points to regress on (test points) (numeric matrix).
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

Matrix containing predicted responses (numeric matrix).

Author(s)

mlpack developers

Examples

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

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