Fits the TabNet: Attentive Interpretable Tabular Learning model

```
tabnet_fit(x, ...)
# Default S3 method
tabnet_fit(x, ...)
# S3 method for class 'data.frame'
tabnet_fit(
x,
y,
tabnet_model = NULL,
config = tabnet_config(),
...,
from_epoch = NULL,
weights = NULL
)
# S3 method for class 'formula'
tabnet_fit(
formula,
data,
tabnet_model = NULL,
config = tabnet_config(),
...,
from_epoch = NULL,
weights = NULL
)
# S3 method for class 'recipe'
tabnet_fit(
x,
data,
tabnet_model = NULL,
config = tabnet_config(),
...,
from_epoch = NULL,
weights = NULL
)
# S3 method for class 'Node'
tabnet_fit(
x,
tabnet_model = NULL,
config = tabnet_config(),
...,
from_epoch = NULL
)
```

- x
Depending on the context:

A

**data frame**of predictors.A

**matrix**of predictors.A

**recipe**specifying a set of preprocessing steps created from`recipes::recipe()`

.A

**Node**where tree will be used as hierarchical outcome, and attributes will be used as predictors.

The predictor data should be standardized (e.g. centered or scaled). The model treats categorical predictors internally thus, you don't need to make any treatment. The model treats missing values internally thus, you don't need to make any treatment.

- ...
Model hyperparameters. Any hyperparameters set here will update those set by the config argument. See

`tabnet_config()`

for a list of all possible hyperparameters.- y
When

`x`

is a**data frame**or**matrix**,`y`

is the outcome specified as:A

**data frame**with 1 or many numeric column (regression) or 1 or many categorical columns (classification) .A

**matrix**with 1 column.A

**vector**, either numeric or categorical.

- tabnet_model
A previously fitted

`tabnet_model`

object to continue the fitting on. if`NULL`

(the default) a brand new model is initialized.- config
A set of hyperparameters created using the

`tabnet_config`

function. If no argument is supplied, this will use the default values in`tabnet_config()`

.- from_epoch
When a

`tabnet_model`

is provided, restore the network weights from a specific epoch. Default is last available checkpoint for restored model, or last epoch for in-memory model.- weights
Unused.

- formula
A formula specifying the outcome terms on the left-hand side, and the predictor terms on the right-hand side.

- data
When a

**recipe**or**formula**is used,`data`

is specified as:A

**data frame**containing both the predictors and the outcome.

A TabNet model object. It can be used for serialization, predictions, or further fitting.

When providing a parent `tabnet_model`

parameter, the model fitting resumes from that model weights
at the following epoch:

last fitted epoch for a model already in torch context

Last model checkpoint epoch for a model loaded from file

the epoch related to a checkpoint matching or preceding the

`from_epoch`

value if provided The model fitting metrics append on top of the parent metrics in the returned TabNet model.

TabNet allows multi-outcome prediction, which is usually named multi-label classification or multi-output regression when outcomes are numerical. Multi-outcome currently expect outcomes to be either all numeric or all categorical.

TabNet uses `torch`

as its backend for computation and `torch`

uses all
available threads by default.

You can control the number of threads used by `torch`

with:

```
data("ames", package = "modeldata")
data("attrition", package = "modeldata")
ids <- sample(nrow(attrition), 256)
## Single-outcome regression using formula specification
fit <- tabnet_fit(Sale_Price ~ ., data = ames, epochs = 1)
## Single-outcome classification using data-frame specification
attrition_x <- attrition[,-which(names(attrition) == "Attrition")]
fit <- tabnet_fit(attrition_x, attrition$Attrition, epochs = 1, verbose = TRUE)
#> [Epoch 001] Loss: 0.995773
## Multi-outcome regression on `Sale_Price` and `Pool_Area` in `ames` dataset using formula,
ames_fit <- tabnet_fit(Sale_Price + Pool_Area ~ ., data = ames[ids,], epochs = 2, valid_split = 0.2)
## Multi-label classification on `Attrition` and `JobSatisfaction` in
## `attrition` dataset using recipe
library(recipes)
#>
#> Attaching package: ‘recipes’
#> The following object is masked from ‘package:stats’:
#>
#> step
rec <- recipe(Attrition + JobSatisfaction ~ ., data = attrition[ids,]) %>%
step_normalize(all_numeric(), -all_outcomes())
attrition_fit <- tabnet_fit(rec, data = attrition[ids,], epochs = 2, valid_split = 0.2)
## Hierarchical classification on `acme`
data(acme, package = "data.tree")
acme_fit <- tabnet_fit(acme, epochs = 2, verbose = TRUE)
#> [Epoch 001] Loss: 2.754614
#> [Epoch 002] Loss: 1.907745
# Note: Dataset number of rows and model number of epochs should be increased
# for publication-level results.
```