RWNN (version 0.4)

boost_rwnn: Boosting random weight neural networks

Description

Use gradient boosting to create ensemble random weight neural network models.

Usage

boost_rwnn(
  formula,
  data = NULL,
  n_hidden = c(),
  lambda = NULL,
  B = 100,
  epsilon = 0.1,
  method = NULL,
  type = NULL,
  control = list()
)
# S3 method for formula
boost_rwnn(
  formula,
  data = NULL,
  n_hidden = c(),
  lambda = NULL,
  B = 100,
  epsilon = 0.1,
  method = NULL,
  type = NULL,
  control = list()
)

Value

An ERWNN-object.

Arguments

formula

A formula specifying features and targets used to estimate the parameters of the output layer.

data

A data-set (either a data.frame or a tibble) used to estimate the parameters of the output layer.

n_hidden

A vector of integers designating the number of neurons in each of the hidden layers (the length of the list is taken as the number of hidden layers).

lambda

The penalisation constant(s) passed to either rwnn or ae_rwnn (see method argument).

B

The number of levels used in the boosting tree.

epsilon

The learning rate.

method

The penalisation type passed to ae_rwnn. Set to NULL (default), "l1", or "l2". If NULL, rwnn is used as the base learner.

type

A string indicating whether this is a regression or classification problem.

control

A list of additional arguments passed to the control_rwnn function.

References

Friedman J.H. (2001) "Greedy function approximation: A gradrient boosting machine." The Annals of Statistics, 29, 1189-1232.

Examples

n_hidden <- 10

B <- 100
epsilon <- 0.1
lambda <- 0.01

m <- boost_rwnn(y ~ ., data = example_data, n_hidden = n_hidden,
                lambda = lambda, B = B, epsilon = epsilon)