Models

This module provides classes for the Mambular models that adhere to scikit-learn’s BaseEstimator interface.

Mambular

Modules	Description
`MambularClassifier`	Multi-class and binary classification tasks with a sequential Mambular Model.
`MambularRegressor`	Regression tasks with a sequential Mambular Model.
`MambularLSS`	Various statistical distribution families for different types of regression and classification tasks.

Modules	Description
`FTTransformerClassifier`	FT transformer for classification tasks.
`FTTransformerRegressor`	FT transformer for regression tasks.
`FTTransformerLSS`	Various statistical distribution families for different types of regression and classification tasks.

Modules	Description
`MLPClassifier`	Multi-class and binary classification tasks.
`MLPRegressor`	MLP for regression tasks.
`MLPLSS`	Various statistical distribution families for different types of regression and classification tasks.

Modules	Description
`TabTransformerClassifier`	TabTransformer for classification tasks.
`TabTransformerRegressor`	TabTransformer for regression tasks.
`TabTransformerLSS`	TabTransformer for distributional tasks.

Modules	Description
`ResNetClassifier`	Multi-class and binary classification tasks using ResNet.
`ResNetRegressor`	Regression tasks using ResNet.
`ResNetLSS`	Distributional tasks using ResNet.

Modules	Description
`MambaTabClassifier`	Multi-class and binary classification tasks using MambaTab.
`MambaTabRegressor`	Regression tasks using MambaTab.
`MambaTabLSS`	Distributional tasks using MambaTab.

Modules	Description
`MambAttentionClassifier`	Multi-class and binary classification tasks using a Combination between Mamba and Attention layers.
`MambAttentionRegressor`	Regression tasks using sing a Combination between Mamba and Attention layers.
`MambAttentionLSS`	Distributional tasks using sing a Combination between Mamba and Attention layers.

Modules	Description
`TabulaRNNClassifier`	Multi-class and binary classification tasks using a RNN.
`TabulaRNNRegressor`	Regression tasks using a RNN.
`TabulaRNNLSS`	Distributional tasks using a RNN.

Modules	Description
`TabMClassifier`	Multi-class and binary classification tasks using TabM - Batch Ensembling MLP.
`TabMRegressor`	Regression tasks using TabM - Batch Ensembling MLP.
`TabMLSS`	Distributional tasks using TabM - Batch Ensembling MLP.

Modules	Description
`NODEClassifier`	Multi-class and binary classification tasks using Neural Oblivious Decision Ensembles.
`NODERegressor`	Regression tasks using Neural Oblivious Decision Ensembles.
`NODELSS`	Distributional tasks using Neural Oblivious Decision Ensembles.

Modules	Description
`NDTFClassifier`	Multi-class and binary classification tasks using a Neural Decision Forest.
`NDTFRegressor`	Regression tasks using a Neural Decision Forest
`NDTFLSS`	Distributional tasks using a Neural Decision Forest.

Modules	Description
`SAINTClassifier`	Multi-class and binary classification tasks using SAINT.
`SAINTRegressor`	Regression tasks using SAINT.
`SAINTLSS`	Distributional tasks using SAINT.

Modules	Description
`SklearnBaseClassifier`	Base class for classification tasks.
`SklearnBaseLSS`	Base class for distributional tasks.
`SklearnBaseRegressor`	Base class for regression tasks.

Warning

Experimental models are available from deeptab.models.experimental. Their API may change without a deprecation cycle.

Modules	Description
`ModernNCAClassifier`	ModernNCA for classification tasks.
`ModernNCARegressor`	ModernNCA for regression tasks.
`ModernNCALSS`	ModernNCA for distributional tasks.
`TangosClassifier`	Tangos for classification tasks.
`TangosRegressor`	Tangos for regression tasks.
`TangosLSS`	Tangos for distributional tasks.
`TromptClassifier`	Trompt for classification tasks.
`TromptRegressor`	Trompt for regression tasks.
`TromptLSS`	Trompt for distributional tasks.

Stable Models

Full API Reference