ClassifierAsRegressor

Bases: RegressorMixin

Wrapper class to use a classifier as a regressor.

This class takes a classifier estimator and converts it into a regressor by encoding the target labels and treating the regression problem as a classification task.

Parameters:

Name	Type	Description	Default
estimator		object Classifier estimator to be used as a regressor.	required

Attributes:

Name	Type	Description
label_encoder_		LabelEncoder Label encoder used to transform target regression labels to classes.
y_train_		array-like of shape (n_samples,) Transformed target labels used for training.
categorical_features		list List of categorical feature indices.

Example

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.model_selection import train_test_split
>>> from tabpfn.scripts.estimator import ManyClassClassifier, TabPFNClassifier, ClassifierAsRegressor
>>> x, y = load_diabetes(return_X_y=True)
>>> x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42)
>>> clf = TabPFNClassifier()
>>> clf = ManyClassClassifier(clf, n_estimators=10, alphabet_size=clf.max_num_classes_)
>>> reg = ClassifierAsRegressor(clf)
>>> reg.fit(x_train, y_train)
>>> y_pred = reg.predict(x_test)

fit

fit(X, y)

Fit the classifier as a regressor.

Parameters:

Name	Type	Description	Default
X		array-like of shape (n_samples, n_features) Training data.	required
y		array-like of shape (n_samples,) Target labels.	required

Returns:

Name	Type	Description
self		object Fitted estimator.

predict

predict(X)

Predict the target values for the input data.

Parameters:

Name	Type	Description	Default
X		array-like of shape (n_samples, n_features) Input data.	required

Returns:

Name	Type	Description
y_pred		array-like of shape (n_samples,) Predicted target values.