For this engine, there are multiple modes: classification and regression
This model has 3 tuning parameters:
hidden_units
: # Hidden Units (type: integer, default: 10L)
penalty
: Amount of Regularization (type: double, default: 0.0)
epochs
: # Epochs (type: integer, default: 1000L)
These defaults are set by the baguette
package and are different than those in [nnet::nnet()].
The baguette extension package is required to fit this model.
library(baguette)
bag_mlp(penalty = double(1), hidden_units = integer(1)) %>%
set_engine("nnet") %>%
set_mode("classification") %>%
translate()
## Bagged Neural Network Model Specification (classification)
##
## Main Arguments:
## hidden_units = integer(1)
## penalty = double(1)
##
## Computational engine: nnet
##
## Model fit template:
## baguette::bagger(formula = missing_arg(), data = missing_arg(),
## weights = missing_arg(), size = integer(1), decay = double(1),
## base_model = "nnet")
The baguette extension package is required to fit this model.
library(baguette)
bag_mlp(penalty = double(1), hidden_units = integer(1)) %>%
set_engine("nnet") %>%
set_mode("regression") %>%
translate()
## Bagged Neural Network Model Specification (regression)
##
## Main Arguments:
## hidden_units = integer(1)
## penalty = double(1)
##
## Computational engine: nnet
##
## Model fit template:
## baguette::bagger(formula = missing_arg(), data = missing_arg(),
## weights = missing_arg(), size = integer(1), decay = double(1),
## base_model = "nnet")
Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
Predictors should have the same scale. One way to achieve this is to center and scale each so that each predictor has mean zero and a variance of one.
The underlying model implementation does not allow for case weights.
Breiman L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
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