TabNet is a neural network architecture for tabular data that uses sequential attention to select features. In practice, it is used as a purpose-built tabular-data library within a deep learning stack, providing an alternative to general frameworks and approaches such as fastai, Keras, and XGBoost. section 8.0 section 8.1
A practical TabNet workflow can reuse established preprocessing pipelines, then reshape their outputs into the NumPy array-of-lists format expected by the model. A `TabNetClassifier` is configured with an optimizer, learning rate, learning-rate scheduler, and mask type before training. section 8.3.1 section 8.3.1
For tabular classification, the implementation centers on these steps:
- Install `pytorch-tabnet` and import PyTorch plus `TabNetClassifier`. section 8.3.1
- Convert pipeline outputs into train, validation, and test NumPy arrays, alongside target arrays. section 8.3.1
- Fit the classifier while evaluating training and validation accuracy. section 8.3.1
- Use `max_epochs` and `patience` to stop training early when validation performance no longer improves. section 8.3.1
Airbnb NYC listing-price workflow
TabNet is applied to the Airbnb NYC prediction problem after the same pipeline definitions used for Keras and XGBoost. Training reports epoch-level loss, training accuracy, and validation accuracy. With a maximum of 10 epochs and patience of 3, training reaches the epoch limit when validation accuracy does not remain unimproved long enough to cross the patience threshold. section 8.3 section 8.3.1
Listing 8.12 Statements to define the TabNet model
tb_cls = TabNetClassifier(optimizer_fn=torch.optim.Adam, #1
optimizer_params=dict(lr=1e-3), #2
scheduler_params={"step_size":10,"gamma":0.9}, #3
scheduler_fn=torch.optim.lr_scheduler.StepLR,
mask_type='entmax' # "sparsemax"
)
The model-definition listing shows the concrete configuration point: Adam optimization, a learning rate and scheduler, plus an `entmax` mask type. section 8.3.1
TabNet is one option among deep-learning libraries for tabular data. Choosing such a library also calls for practical assessment: whether it is easy to use and works with current deep-learning frameworks, rather than relying only on published research or popularity. section 1.3 section 3.4
