man/rmd/bag_mlp_nnet.md

In parsnip: A Common API to Modeling and Analysis Functions

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 \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.

Case weights

The underlying model implementation does not allow for case weights.

References

• Breiman L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

parsnip documentation built on Aug. 18, 2023, 1:07 a.m.