`mlp()`

defines a multilayer perceptron model (a.k.a. a single layer,
feed-forward neural network).

There are different ways to fit this model. See the engine-specific pages for more details:

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

mlp( mode = "unknown", engine = "nnet", hidden_units = NULL, penalty = NULL, dropout = NULL, epochs = NULL, activation = NULL )

mode | A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification". |
---|---|

engine | A single character string specifying what computational engine to use for fitting. |

hidden_units | An integer for the number of units in the hidden model. |

penalty | A non-negative numeric value for the amount of weight decay. |

dropout | A number between 0 (inclusive) and 1 denoting the proportion of model parameters randomly set to zero during model training. |

epochs | An integer for the number of training iterations. |

activation | A single character string denoting the type of relationship between the original predictors and the hidden unit layer. The activation function between the hidden and output layers is automatically set to either "linear" or "softmax" depending on the type of outcome. Possible values are: "linear", "softmax", "relu", and "elu" |

This function only defines what *type* of model is being fit. Once an engine
is specified, the *method* to fit the model is also defined.

The model is not trained or fit until the `fit.model_spec()`

function is used
with the data.

https://www.tidymodels.org, *Tidy Models with R*

#> # A tibble: 4 × 2 #> engine mode #> <chr> <chr> #> 1 keras classification #> 2 keras regression #> 3 nnet classification #> 4 nnet regressionmlp(mode = "classification", penalty = 0.01)#> Single Layer Neural Network Specification (classification) #> #> Main Arguments: #> penalty = 0.01 #> #> Computational engine: nnet #>