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

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

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

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

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

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

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

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

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

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.

tabnet documentation built on May 31, 2023, 6:27 p.m.