Overview

DeepTab is a Python library that brings modern deep learning architectures to tabular data. It wraps PyTorch and Lightning behind a scikit-learn-compatible interface, so you can use state-of-the-art models without changing how you already work with data.

Why DeepTab

Tabular data is the most common format in applied machine learning, yet most deep learning tooling is designed for images or text. DeepTab fills that gap by:

• Providing a consistent fit / predict / evaluate API across all models.

• Handling categorical encoding, numerical preprocessing, and batching automatically.

• Supporting regression, classification, and distributional regression from the same model class.

• Integrating with scikit-learn pipelines and hyperparameter search tools.

Available models

All models support regression, classification, and distributional regression out of the box. Import them as <ModelName>Regressor, <ModelName>Classifier, or <ModelName>LSS.

Model	Architecture	Reference
Mambular	Sequential Mamba (SSM) blocks for tabular data	Thielmann et al. (2024)
MambaTab	Mamba block on a joint input representation	Ahamed et al. (2024)
MambAttention	Mamba + Transformer hybrid	Thielmann et al. (2025)
FTTransformer	Feature tokeniser + Transformer encoder	Gorishniy et al. (2021)
TabTransformer	Transformer with categorical embeddings	Huang et al. (2020)
SAINT	Row attention + contrastive pre-training	Somepalli et al. (2021)
TabM	Batch ensembling for MLP	Gorishniy et al. (2024)
TabR	Retrieval-augmented tabular model	—
ResNet	ResNet adapted for tabular data	—
MLP	Multi-layer perceptron baseline	—
NODE	Neural oblivious decision ensembles	Popov et al. (2019)
NDTF	Neural decision tree forest	Kontschieder et al. (2015)
TabulaRNN	Recurrent neural network for tabular data	Thielmann et al. (2025)
ENODE	Extended NODE variant	—
AutoInt	Automatic feature interaction via attention	—
ModernNCA	Modern neural classification architecture	—
Trompt	Tabular-specific prompting model	—
TANGOS	Tabular model with graph-based structure	—

Next steps

• Installation — install DeepTab and verify the setup.

• Key Concepts — understand the API patterns before writing code.

• Examples — runnable end-to-end workflows.

• API Reference — full parameter documentation.