RWNN (version 0.4)

bag_rwnn: Bagging random weight neural networks

Description

Use bootstrap aggregation to reduce the variance of random weight neural network models.

Usage

bag_rwnn(
  formula,
  data = NULL,
  n_hidden = c(),
  lambda = NULL,
  B = 100,
  method = NULL,
  type = NULL,
  control = list()
)
# S3 method for formula
bag_rwnn(
  formula,
  data = NULL,
  n_hidden = c(),
  lambda = NULL,
  B = 100,
  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 bootstrap samples.

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

Breiman L. (1996) "Bagging Predictors." Machine Learning, 24, 123-140.

Breiman L. (2001) "Random Forests." Machine Learning, 45, 5-32.

Sui X, He S, Vilsen SB, Teodorescu R, Stroe DI (2021) "Fast and Robust Estimation of Lithium-ion Batteries State of Health Using Ensemble Learning." In 2021 IEEE Energy Conversion Congress and Exposition (ECCE), 1-8.

Examples

n_hidden <- 50

B <- 100
lambda <- 0.01

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