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 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 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.model_spec(), 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.

Examples

The “Fitting and Predicting with parsnip” article contains examples for mlp() with the "keras" engine.

References

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