nnet::nnet() fits a single layer, feed-forward neural network.
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: none)penalty: Amount of Regularization (type: double, default: 0.0)epochs: # Epochs (type: integer, default: 100L)
Note that, in nnet::nnet(), the maximum number of
parameters is an argument with a fairly low value of maxit = 1000. For
some models, you may need to pass this value in via
set_engine() so that the model does not fail.
Translation from parsnip to the original package (regression)
mlp(
hidden_units = integer(1),
penalty = double(1),
epochs = integer(1)
) %>%
set_engine("nnet") %>%
set_mode("regression") %>%
translate()## Single Layer Neural Network Model Specification (regression)
##
## Main Arguments:
## hidden_units = integer(1)
## penalty = double(1)
## epochs = integer(1)
##
## Computational engine: nnet
##
## Model fit template:
## nnet::nnet(formula = missing_arg(), data = missing_arg(), size = integer(1),
## decay = double(1), maxit = integer(1), trace = FALSE, linout = TRUE)Note that parsnip automatically sets linear activation in the last layer.
Translation from parsnip to the original package (classification)
mlp(
hidden_units = integer(1),
penalty = double(1),
epochs = integer(1)
) %>%
set_engine("nnet") %>%
set_mode("classification") %>%
translate()## Single Layer Neural Network Model Specification (classification)
##
## Main Arguments:
## hidden_units = integer(1)
## penalty = double(1)
## epochs = integer(1)
##
## Computational engine: nnet
##
## Model fit template:
## nnet::nnet(formula = missing_arg(), data = missing_arg(), size = integer(1),
## decay = double(1), maxit = integer(1), trace = FALSE, linout = FALSE)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.
Saving fitted model objects
This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.