tabnet_params: Parameters for the tabnet model

In tabnet: Fit 'TabNet' Models for Classification and Regression

Parameters for the tabnet model

Description

Parameters for the tabnet model

Usage

attention_width(range = c(8L, 64L), trans = NULL)

decision_width(range = c(8L, 64L), trans = NULL)

feature_reusage(range = c(1, 2), trans = NULL)

momentum(range = c(0.01, 0.4), trans = NULL)

mask_type(values = c("sparsemax", "entmax"))

num_independent(range = c(1L, 5L), trans = NULL)

num_shared(range = c(1L, 5L), trans = NULL)

num_steps(range = c(3L, 10L), trans = NULL)

Arguments

range	the default range for the parameter value
trans	whether to apply a transformation to the parameter
values	possible values for factor parameters These functions are used with tune grid functions to generate candidates.

Value

A dials parameter to be used when tuning TabNet models.

Examples

  model <- tabnet(attention_width = tune(), feature_reusage = tune(),
    momentum = tune(), penalty = tune(), rate_step_size = tune()) %>%
    parsnip::set_mode("regression") %>%
    parsnip::set_engine("torch")

tabnet documentation built on April 17, 2025, 1:07 a.m.