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Node
dataset. (#126)tabnet_pretrain()
now allows different GLU blocks in GLU layers in encoder and in decoder through the config()
parameters num_idependant_decoder
and num_shared_decoder
(#129)reduce_on_plateau
as option for lr_scheduler
at tabnet_config()
(@SvenVw, #120)autoplot.tabnet_fit()
(#67)tabnet_pretrain()
now allows missing values in predictors. (#68)tabnet_explain()
now works for tabnet_pretrain
models. (#68)random_obfuscator()
torch_nn module. (#68)tabnet_fit()
and predict()
now allow missing values in predictors. (#76)tabnet_config()
now supports a num_workers=
parameters to control parallel dataloading (#83)tabnet_config()
now has a flag skip_importance
to skip calculating feature importance (@egillax, #91)tabnet_nn
min_grid.tabnet
method for tune
(@cphaarmeyer, #107)tabnet_explain()
method for parsnip models (@cphaarmeyer, #108)tabnet_fit()
and predict()
now allow multi-outcome, all numeric or all factors but not mixed. (#118)tabnet_explain()
is now correctly handling missing values in predictors. (#77)dataloader
can now use num_workers>0
(#83)batch_size
and virtual_batch_size
improves performance on mid-range devices.engine="torch"
to tabnet parsnip model (#114)autoplot()
warnings turned into errors with {ggplot2} v3.4 (#113)update
method for tabnet models to allow the correct usage of finalize_workflow
(#60).tabnet_fit()
(@cregouby, #26)tabnet_explain()
.tabnet_pretrain()
for unsupervised pretraining (@cregouby, #29)autoplot()
of model loss among epochs (@cregouby, #36)config
argument to fit() / pretrain()
so one can pass a pre-made config list. (#42)tabnet_config()
, new mask_type
option with entmax
additional to default sparsemax
(@cmcmaster1, #48)tabnet_config()
, loss
now also takes function (@cregouby, #55)NEWS.md
file to track changes to the package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.