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Residual Neural Network for Tabular Data

Source: R/tabular_resnet.R
tabular_resnet.Rd

tabular_resnet() ... This function can fit classification and regression models.

There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

¹ The default engine. ² Requires a parsnip extension package for classification and regression.

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

Usage

tabular_resnet(
  mode = "unknown",
  engine = "brulee",
  hidden_units = NULL,
  bottleneck_units = NULL,
  residual_at = NULL,
  penalty = NULL,
  mixture = NULL,
  dropout = NULL,
  epochs = NULL,
  activation = NULL,
  learn_rate = NULL,
  rate_schedule = NULL,
  momentum = NULL,
  batch_size = NULL,
  class_weights = NULL,
  stop_iter = NULL
)

Arguments

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 vector for the number of units in the hidden model.

bottleneck_units

The number of embeddings that are produced by batch normalization.

residual_at

An integer vector with the layer number should use a residual connection (i.e., skip layer).

penalty

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

mixture

A number between zero and one (inclusive) giving the proportion of L1 regularization (i.e. lasso) in the model. mixture = 1 is a pure lasso model while mixture = 0 indicates ridge regression (a.k.a 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 vector character strings denoting the type of relationship between the layers. 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 depend on the engine being used.

learn_rate

A number for the rate at which the boosting algorithm adapts from iteration-to-iteration (specific engines only). This is sometimes referred to as the shrinkage parameter.

rate_schedule

A character string for the learning rate schedule.

momentum

A number for the momentum parameter in optimizers that use it.

batch_size

An integer for the number of training instances in each batch.

class_weights

Numeric class weights for imbalanced data (classification only).

stop_iter

The number of iterations without improvement before stopping (specific engines only).

See also

fit(), set_engine(), update(), brulee engine details

Examples

show_engines("tabular_resnet")
#> # A tibble: 0 × 2
#> # ℹ 2 variables: engine <chr>, mode <chr>

tabular_resnet(mode = "classification", penalty = 0.01)
#> ! parsnip could not locate an implementation for `tabular_resnet`
#>   classification model specifications.
#> ℹ The parsnip extension package tabby implements support for this
#>   specification.
#> ℹ Please install (if needed) and load to continue.
#> tabular resnet Model Specification (classification)
#> 
#> Main Arguments:
#>   penalty = 0.01
#> 
#> Computational engine: brulee 
#>