R: Fit 'TabNet' Models for Classification and Regression

# Fit 'TabNet' Models for Classification and Regression

## Documentation for package ‘tabnet’ version 0.9.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

attention_width | Parameters for the tabnet model
augment.tabnet_fit | Predict using 'tabnet'
autoplot.tabnet_explain | Plot tabnet_explain mask importance heatmap
autoplot.tabnet_fit | Plot tabnet_fit model loss along epochs
autoplot.tabnet_pretrain | Plot tabnet_fit model loss along epochs
build_ancestor_matrix_from_outcomes | Build ancestor matrix aligned with observed outcome classes
cat_emb_dim | Non-tunable parameters for the tabnet model
checkpoint_epochs | Non-tunable parameters for the tabnet model
check_compliant_node | Check that Node object names are compliant
decision_width | Parameters for the tabnet model
drop_last | Non-tunable parameters for the tabnet model
encoder_activation | Non-tunable parameters for the tabnet model
entmax | Alpha-entmax
entmax15 | Alpha-entmax
feature_reusage | Parameters for the tabnet model
get_constr_output | Apply hierarchy constraints via max-pooling over descendants (MCM)
get_tau | Optimal threshold (tau) computation for 1.5-entmax
lr_scheduler | Non-tunable parameters for the tabnet model
mask_type | Parameters for the tabnet model
mlp_activation | Non-tunable parameters for the tabnet model
mlp_hidden_multiplier | Non-tunable parameters for the tabnet model
momentum | Parameters for the tabnet model
nnf_mc_loss | Max-Constraint Margin Loss (functional)
nnf_multilabel_one_hot | Convert class_id tensor to binary one-hot tensor
nn_aum_loss | AUM loss
nn_mc_loss | Max-Constraint Margin Loss (module)
nn_prune_head.tabnet_fit | Prune top layer(s) of a tabnet network
nn_prune_head.tabnet_pretrain | Prune top layer(s) of a tabnet network
node_to_df | Turn a Node object into predictor and outcome.
num_independent | Parameters for the tabnet model
num_independent_decoder | Non-tunable parameters for the tabnet model
num_shared | Parameters for the tabnet model
num_shared_decoder | Non-tunable parameters for the tabnet model
num_steps | Parameters for the tabnet model
optimizer | Non-tunable parameters for the tabnet model
penalty | Non-tunable parameters for the tabnet model
predict.tabnet_fit | Predict using 'tabnet'
sparsemax | Sparsemax
sparsemax15 | Sparsemax
tabnet | Parsnip compatible tabnet model
tabnet_config | Configuration for TabNet models
tabnet_explain | Interpretation metrics from a TabNet model
tabnet_explain.default | Interpretation metrics from a TabNet model
tabnet_explain.model_fit | Interpretation metrics from a TabNet model
tabnet_explain.tabnet_fit | Interpretation metrics from a TabNet model
tabnet_explain.tabnet_pretrain | Interpretation metrics from a TabNet model
tabnet_fit | Tabnet model
tabnet_fit.data.frame | Tabnet model
tabnet_fit.default | Tabnet model
tabnet_fit.formula | Tabnet model
tabnet_fit.Node | Tabnet model
tabnet_fit.recipe | Tabnet model
tabnet_nn | TabNet Model Architecture
tabnet_pretrain | Tabnet model
tabnet_pretrain.data.frame | Tabnet model
tabnet_pretrain.default | Tabnet model
tabnet_pretrain.formula | Tabnet model
tabnet_pretrain.Node | Tabnet model
tabnet_pretrain.recipe | Tabnet model
verbose | Non-tunable parameters for the tabnet model
virtual_batch_size | Non-tunable parameters for the tabnet model