Update finetuned_regressor.py

CHANGELOG.md

@@ -27,6 +27,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - - Ensure `TabPFNValidationError` wraps both custom and sklearn's validate_data() errors ([#732](https://github.com/PriorLabs/TabPFN/pull/732))
 - Refactor of model encoder. Move imports from `tabpfn.architectures.base.encoders` to `tabpfn.architectures.encoders` ([#733](https://github.com/PriorLabs/TabPFN/pull/733))
 - Renamed the estimator's `preprocessor_` attribute to `ordinal_encoder_` ([#756](https://github.com/PriorLabs/TabPFN/pull/756))
+- Pass through kwargs in `FinetunedTabPFNClassifier` and `FinetunedTabPFNRegressor` predict and predict_proba methods to allow additional options like `output_type='full'` ([#772](https://github.com/PriorLabs/TabPFN/pull/772)) 
 
 
 ## [6.3.1] - 2026-01-14

changelog/772.added.md

@@ -0,0 +1 @@
+Pass through kwargs in FinetunedTabPFNClassifier and FinetunedTabPFNRegressor predict and predict_proba methods to allow additional options like output_type='full'

src/tabpfn/finetuning/finetuned_classifier.py

@@ -398,30 +398,34 @@ def fit(
         super().fit(X, y, X_val=X_val, y_val=y_val, output_dir=output_dir)
         return self
 
-    def predict_proba(self, X: XType) -> np.ndarray:
+    def predict_proba(self, X: XType, **kwargs) -> np.ndarray:
         """Predict class probabilities for X.
 
         Args:
             X: The input samples of shape (n_samples, n_features).
+            **kwargs: Additional keyword arguments to pass to the underlying
+                inference classifier.
 
         Returns:
             The class probabilities of the input samples with shape
             (n_samples, n_classes).
         """
         check_is_fitted(self)
 
-        return self.finetuned_inference_classifier_.predict_proba(X)  # type: ignore
+        return self.finetuned_inference_classifier_.predict_proba(X, **kwargs)  # type: ignore
 
     @override
-    def predict(self, X: XType) -> np.ndarray:
+    def predict(self, X: XType, **kwargs) -> np.ndarray:
         """Predict the class for X.
 
         Args:
             X: The input samples of shape (n_samples, n_features).
+            **kwargs: Additional keyword arguments to pass to the underlying
+                inference classifier.
 
         Returns:
             The predicted classes with shape (n_samples,).
         """
         check_is_fitted(self)
 
-        return self.finetuned_inference_classifier_.predict(X)  # type: ignore
+        return self.finetuned_inference_classifier_.predict(X, **kwargs)  # type: ignore

src/tabpfn/finetuning/finetuned_regressor.py

@@ -28,6 +28,7 @@
 if TYPE_CHECKING:
     from tabpfn.constants import XType, YType
     from tabpfn.finetuning.data_util import RegressorBatch
+    from tabpfn.regressor import RegressionResultType
 
 
 def _compute_regression_loss(  # noqa: C901
@@ -567,15 +568,17 @@ def fit(
         return self
 
     @override
-    def predict(self, X: XType) -> np.ndarray:
+    def predict(self, X: XType, **kwargs) -> RegressionResultType:
         """Predict target values for X.
 
         Args:
             X: The input samples of shape (n_samples, n_features).
+            **kwargs: Additional keyword arguments to pass to the underlying
+                inference regressor (e.g., output_type, quantiles).
 
         Returns:
             The predicted target values with shape (n_samples,).
         """
         check_is_fitted(self)
 
-        return self.finetuned_inference_regressor_.predict(X)
+        return self.finetuned_inference_regressor_.predict(X, **kwargs)