mlpack (version 4.8.0)

lars_train: LARS Training

Description

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

Usage

lars_train(
  input,
  responses,
  lambda1 = 0,
  lambda2 = 0,
  no_intercept = FALSE,
  no_normalize = FALSE,
  use_cholesky = FALSE,
  verbose = getOption("mlpack.verbose", FALSE)
)

Value

A list with several components defining the class attributes:

output_model

Output LARS model (LARS).

Arguments

input

Matrix of covariates (X) (numeric matrix).

responses

Row vector of responses/observations (y) (numeric row).

lambda1

Regularization parameter for l1-norm penalty. Default value "0" (numeric).

lambda2

Regularization parameter for l2-norm penalty. Default value "0" (numeric).

no_intercept

Do not fit an intercept in the model. Default value "FALSE" (logical).

no_normalize

Do not normalize data to unit variance before modeling. Default value "FALSE" (logical).

use_cholesky

Use Cholesky decomposition during computation rather than explicitly computing the full Gram matrix. Default value "FALSE" (logical).

verbose

Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical).

Author

mlpack developers

Details

An implementation of LARS: Least Angle Regression (stagewise/lasso). This is a stage-wise homotopy-based algorithm for L1-regularized linear regression (LASSO) and L1+L2-regularized linear regression (Elastic Net).

This program is able to train a LARS/LASSO/Elastic Net model or load a model from a file, output regression predictions for a test set, and save the trained model to a file. The LARS algorithm is described in more detail below:

Let X be a matrix where each row is a point and each column is a dimension, and let y be a vector of targets.

The Elastic Net problem is to solve

min_beta 0.5 || X * beta - y ||_2^2 + lambda_1 ||beta||_1 + 0.5 lambda_2 ||beta||_2^2

If lambda1 > 0 and lambda2 = 0, the problem is the LASSO. If lambda1 > 0 and lambda2 > 0, the problem is the Elastic Net. If lambda1 = 0 and lambda2 > 0, the problem is ridge regression. If lambda1 = 0 and lambda2 = 0, the problem is unregularized linear regression.

For efficiency reasons, it is not recommended to use this algorithm with "lambda1" = 0. In that case, use the 'linear_regression' program, which implements both unregularized linear regression and ridge regression.

To train a LARS/LASSO/Elastic Net model, the "input" and "responses" parameters must be given. The "lambda1", "lambda2", and "use_cholesky" parameters control the training options. A trained model can be saved with the "output_model". If no training is desired at all, a model can be passed via the "input_model" parameter.

Examples

# 
# #' # \dontrun{
# suppressMessages(library(mlpack)) # in case 'mlpack' is not yet loaded
# X <- as.matrix(read.csv("http://datasets.mlpack.org/admission_predict.csv",
# header=FALSE))
# y <-
# as.matrix(read.csv("http://datasets.mlpack.org/admission_predict.responses.
# 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 <- lars_train(input=X_train, responses=y_train, lambda1=1e-05,
#   lambda2=1e-06)
#   }