Key Concepts

This page explains the mental model behind DeepTab before you write any code.

scikit-learn-compatible API

Every DeepTab model implements the scikit-learn BaseEstimator interface. If you have used scikit-learn before, the workflow is identical:

model = MambularClassifier()      # 1. instantiate
model.fit(X_train, y_train)       # 2. fit
predictions = model.predict(X_test)  # 3. predict
metrics = model.evaluate(X_test, y_test)  # 4. evaluate

X can be a pandas DataFrame or a NumPy array. DeepTab handles the conversion internally.

Task variants

Each model ships in three variants selected by the class suffix:

Suffix	Task	Output
`Classifier`	Classification	Class labels and probabilities
`Regressor`	Regression	Continuous point estimates
`LSS`	Distributional regression	Full distribution parameters

Switching tasks requires only changing the import — the rest of the code is identical:

from deeptab.models import MambularClassifier   # classification
from deeptab.models import MambularRegressor    # regression
from deeptab.models import MambularLSS          # distributional regression

Stable vs experimental models

DeepTab ships models at two tiers:

Tier	Import path	Guarantee
Stable	`from deeptab.models import ...`	Public API frozen under semantic versioning
Experimental	`from deeptab.models.experimental import ...`	May change without a deprecation cycle

Always use the explicit experimental import path to signal that you accept the instability:

# stable
from deeptab.models import FTTransformerClassifier

# experimental — explicit path required
from deeptab.models.experimental import TromptClassifier

See Using experimental models for a full worked example.

Configuring hyperparameters

Every model has a corresponding config class in deeptab.configs that documents all available hyperparameters. You can either pass hyperparameters directly to the constructor or via a config object:

from deeptab.configs import MambularConfig
from deeptab.models import MambularClassifier

# Option A: keyword arguments
model = MambularClassifier(d_model=64, n_layers=4, dropout=0.1)

# Option B: config object — same result, easier to version and share
config = MambularConfig(d_model=64, n_layers=4, dropout=0.1)
model = MambularClassifier(config=config)

Fit arguments

Training arguments such as learning rate, batch size, and epochs are passed to fit, not the constructor. This keeps architecture hyperparameters separate from training hyperparameters:

model.fit(
    X_train,
    y_train,
    max_epochs=100,
    lr=1e-3,
    batch_size=256,
)

Distributional regression (LSS)

LSS models predict the parameters of a parametric distribution rather than a single value. Specify the output family via the family argument of fit:

model = MambularLSS()
model.fit(X_train, y_train, family="normal")  # learns μ and σ per sample

Common families: "normal", "poisson", "gamma", "beta". See the API reference for the full list.

Data preprocessing

DeepTab detects column types automatically from the DataFrame and applies appropriate preprocessing:

Numerical columns — standardised by default.
Categorical columns — ordinally encoded and embedded.
Missing values — handled internally; no need to impute before passing data.

You can override the preprocessing strategy via config parameters if needed.