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This Many Class Classifier Extension allows TabPFN to handle classification problems with more classes than TabPFN’s default limit (currently 10 classes). It works through an error-correcting output code (ECOC) approach that:
  • Encodes the multi-class task into multiple binary or small-class subtasks.
  • Trains the base TabPFNClassifier on these subtasks.
  • Decodes the results back into the original class space.
This approach enables TabPFN to scale to hundreds of classes efficiently while maintaining accuracy and calibration.

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Getting Started

Install the many_class extension: 
pip install "tabpfn-extensions[many_class]"
Then, simply wrap your existing TabPFNClassifier with ManyClassClassifier to enable support for datasets with large number of classes. 
from tabpfn_extensions.many_class import ManyClassClassifier
from tabpfn import TabPFNClassifier

estimator = TabPFNClassifier(device="cuda")
classifier = ManyClassClassifier(
    estimator=estimator,
    # The parameters below are optional — shown here for clarity.
    n_estimators_redundancy=4, # Multiplier on the auto-chosen number of sub-estimators; higher = more stable, slower.
    random_state=42,
)
classifier.fit(X_train, y_train)
predictions = classifier.predict(X_test)

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Key parameters

All parameters below are optional — sensible defaults are used if they are not provided.
  • alphabet_size — number of classes each sub-estimator is trained on. Leave unset (the default) so it is inferred from the base estimator’s max_num_classes_ attribute — this way future TabPFN models that support more classes per sub-task will automatically be used to their full capacity. Should not exceed the base estimator’s class limit (10 for TabPFNClassifier with model version ≤ 2.6).
  • n_estimators_redundancy — redundancy multiplier on the minimum number of sub-estimators needed to cover all classes. Higher values improve accuracy and calibration at the cost of runtime. Default is 4.
  • n_estimators — set this to override the auto-derived number of sub-estimators entirely. Leave as None to let it be chosen from alphabet_size, the number of classes in the training data, and n_estimators_redundancy.
  • random_state — controls the randomness of the sub-task encoding, ensuring reproducible results.