baguette::bagger() creates a collection of neural networks forming an
ensemble. All trees in the ensemble are combined to produce a final prediction.
Details
For this engine, there are multiple modes: classification and regression
Tuning Parameters
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().
Translation from parsnip to the original package (classification)
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")Translation from parsnip to the original package (regression)
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")Preprocessing requirements
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 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.