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