bag_rwnn {RWNN} | R Documentation |
Use bootstrap aggregation to reduce the variance of random weight neural network models.
bag_rwnn(
formula,
data = NULL,
n_hidden = c(),
lambda = NULL,
B = 100,
method = NULL,
type = NULL,
control = list()
)
## S3 method for class 'formula'
bag_rwnn(
formula,
data = NULL,
n_hidden = c(),
lambda = NULL,
B = 100,
method = NULL,
type = NULL,
control = list()
)
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. |
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 |
B |
The number of bootstrap samples. |
method |
The penalisation type passed to ae_rwnn. Set to |
type |
A string indicating whether this is a regression or classification problem. |
control |
A list of additional arguments passed to the control_rwnn function. |
An ERWNN-object.
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.
n_hidden <- 50
B <- 100
lambda <- 0.01
m <- bag_rwnn(y ~ ., data = example_data, n_hidden = n_hidden, lambda = lambda, B = B)