Fit 'TabNet' Models for Classification and Regression
Plot tabnet_explain mask importance heatmap
Plot tabnet_fit model loss along epochs
Check that Node object names are compliant
Alpha-entmax
Optimal threshold (tau) computation for 1.5-entmax
Determine the minimum set of model fits
AUM loss
Prune top layer(s) of a tabnet network
Turn a Node object into predictor and outcome.
Pipe operator
Sparsemax
Configuration for TabNet models
Interpretation metrics from a TabNet model
Tabnet model
TabNet Model Architecture
Non-tunable parameters for the tabnet model
Parameters for the tabnet model
Tabnet model
Parsnip compatible tabnet model
Implements the 'TabNet' model by Sercan O. Arik et al. (2019) <doi:10.48550/arXiv.1908.07442> with 'Coherent Hierarchical Multi-label Classification Networks' by Giunchiglia et al. <doi:10.48550/arXiv.2010.10151> and provides a consistent interface for fitting and creating predictions. It's also fully compatible with the 'tidymodels' ecosystem.
Useful links