Models

Scikit-learn compatible estimators for tabular deep learning. Every model implements the BaseEstimator interface and ships in three task variants:

Classifier: binary and multi-class classification
Regressor: point-estimate regression
LSS: distributional regression (Location, Scale, Shape)

from deeptab.models import MambularClassifier

model = MambularClassifier()
model.fit(X_train, y_train, max_epochs=50)
predictions = model.predict(X_test)
probabilities = model.predict_proba(X_test)
metrics = model.evaluate(X_test, y_test)

For model descriptions, comparisons, and tuned configurations, see the Stable Models.

Stable Models

Each architecture provides Classifier, Regressor, and LSS variants.

Architecture	Summary
`Mambular`	Multi-layer Mamba. Strong default.
`MambaTab`	Single Mamba block. Fast.
`MambAttention`	Hybrid Mamba and attention.
`FTTransformer`	Feature-tokenizer transformer.
`TabTransformer`	Transformer for categorical-heavy data.
`SAINT`	Row and column attention.
`ResNet`	Residual MLP.
`MLP`	Plain MLP baseline.
`TabM`	Batch-ensembling MLP.
`AutoInt`	Automatic feature interactions.
`NODE`	Neural oblivious decision ensembles.
`ENODE`	Enhanced NODE.
`NDTF`	Neural decision tree forest.
`TabR`	Retrieval-augmented model.
`TabulaRNN`	RNN over feature sequences.

Experimental Models

Warning

Experimental models live in deeptab.models.experimental. Their API may change without a deprecation cycle, so pin your DeepTab version when using them.

Architecture	Summary
`ModernNCA`	Modern neighborhood component analysis.
`Tangos`	Tangent-based regularization.
`Trompt`	Prompt-based transformer.

Reference

Model Reference