Fitting and prediction

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`TabPFNClassifier.fit`

Fit the model. Uploads the training data to the server, which fits the model on it.

TabPFNClassifier.fit(
    X: pd.DataFrame | np.ndarray,
    y: pd.Series | np.ndarray,
    description: str | None = None,
)

Parameters

Parameter	Type	Default	Description
`X`	`pd.DataFrame \| np.ndarray`	Required	The input data.
`y`	`pd.Series \| np.ndarray`	Required	The target variable.
`description`	`str \| None`	`None`	Description of the dataset and task for the server.

Returns

Type	Description
—	self

Raises ValueError If the data or the estimator’s parameters are invalid, or the data exceeds the model limits.

`TabPFNClassifier.predict`

View source

Predict the class labels for the provided input samples.

TabPFNClassifier.predict(
    X,
)

Parameters

Parameter	Type	Default	Description
`X`	—	Required	The input data for prediction.

Returns

Type	Description
—	The predicted class labels as a NumPy array.

`TabPFNClassifier.predict_proba`

View source

Predict the probabilities of the classes for the provided input samples.

TabPFNClassifier.predict_proba(
    X,
)

Parameters

Parameter	Type	Default	Description
`X`	—	Required	The input data for prediction.

Returns

Type	Description
—	The predicted probabilities of the classes as a NumPy array. Shape (n_samples, n_classes).

`TabPFNClassifier.get_timings`

View source

Seconds the server spent on the last fit and the last prediction. Returns {"fit": ..., "predict": ...}:

fit: elapsed_s, queue_wait_s, train_set_transform_s, fit_s.
predict: test_set_transform_queue_wait_s, test_set_transform_s, predict_queue_wait_s, predict_s. A prediction sent in several requests reports their sum.

Queue wait is time spent waiting for the server to start the work. An entry is None before the first call, on a model restored with load_model(), and when the server does not report timings.

TabPFNClassifier.get_timings() -> dict[str, dict[str, Any] | None]

Returns

Type	Description
`dict[str, dict[str, Any] \| None]`	—

`TabPFNClassifier.last_predict_timings`

View source

Seconds per stage of the most recent prediction, or None. Returns

Type	Description
`dict[str, Any] \| None`	—

Scikit-learn scoring

Inherited score(X, y, sample_weight=None) calls predict and returns mean classification accuracy.

Reference

tabpfn

tabpfn-client

tabpfn-extensions

REST API

Fitting and prediction

`TabPFNClassifier.fit`

`TabPFNClassifier.predict`

`TabPFNClassifier.predict_proba`

`TabPFNClassifier.get_timings`

`TabPFNClassifier.last_predict_timings`

Scikit-learn scoring

​TabPFNClassifier.fit

​TabPFNClassifier.predict

​TabPFNClassifier.predict_proba

​TabPFNClassifier.get_timings

​TabPFNClassifier.last_predict_timings

​Scikit-learn scoring

`TabPFNClassifier.fit`

`TabPFNClassifier.predict`

`TabPFNClassifier.predict_proba`

`TabPFNClassifier.get_timings`

`TabPFNClassifier.last_predict_timings`

Scikit-learn scoring