keras_mlp() fits a single layer, feed-forward neural network.
Details
For this engine, there are multiple modes: classification and regression
Tuning Parameters
This model has 5 tuning parameters:
hidden_units: # Hidden Units (type: integer, default: 5L)penalty: Amount of Regularization (type: double, default: 0.0)dropout: Dropout Rate (type: double, default: 0.0)epochs: # Epochs (type: integer, default: 20L)activation: Activation Function (type: character, default: ‘softmax’)
Translation from parsnip to the original package (regression)
mlp(
hidden_units = integer(1),
penalty = double(1),
dropout = double(1),
epochs = integer(1),
activation = character(1)
) %>%
set_engine("keras") %>%
set_mode("regression") %>%
translate()## Single Layer Neural Network Model Specification (regression)
##
## Main Arguments:
## hidden_units = integer(1)
## penalty = double(1)
## dropout = double(1)
## epochs = integer(1)
## activation = character(1)
##
## Computational engine: keras
##
## Model fit template:
## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = integer(1),
## penalty = double(1), dropout = double(1), epochs = integer(1),
## activation = character(1))Translation from parsnip to the original package (classification)
mlp(
hidden_units = integer(1),
penalty = double(1),
dropout = double(1),
epochs = integer(1),
activation = character(1)
) %>%
set_engine("keras") %>%
set_mode("classification") %>%
translate()## Single Layer Neural Network Model Specification (classification)
##
## Main Arguments:
## hidden_units = integer(1)
## penalty = double(1)
## dropout = double(1)
## epochs = integer(1)
## activation = character(1)
##
## Computational engine: keras
##
## Model fit template:
## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = integer(1),
## penalty = double(1), dropout = double(1), epochs = integer(1),
## activation = character(1))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
Models fitted with this engine may require native serialization methods to be properly saved and/or passed between R sessions. To learn more about preparing fitted models for serialization, see the bundle package.