classifier_as_regressor ¶

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_extensions 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.

get_optimization_mode ¶

get_optimization_mode()

Get the optimization mode for the regressor.

Returns:

Type	Description
	str Optimization mode ("mean").

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.

predict_full ¶

predict_full(X)

Predict the full set of output values for the input data.

Parameters:

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

Returns:

Type	Description
	dict Dictionary containing the predicted output values, including: - "mean": Predicted mean values. - "median": Predicted median values. - "mode": Predicted mode values. - "logits": Predicted logits. - "buckets": Predicted bucket probabilities. - "quantile_{q:.2f}": Predicted quantile values for each quantile q.

probabilities_to_logits_multiclass `staticmethod` ¶

probabilities_to_logits_multiclass(
    probabilities, eps=1e-06
)

Convert probabilities to logits for a multi-class problem.

Parameters:

Name	Type	Description	Default
`probabilities`		array-like of shape (n_samples, n_classes) Input probabilities for each class.	required
`eps`		float, default=1e-6 Small value to avoid division by zero or taking logarithm of zero.	`1e-06`

Returns:

Name	Type	Description
`logits`		array-like of shape (n_samples, n_classes) Output logits for each class.

set_categorical_features ¶

set_categorical_features(categorical_features)

Set the categorical feature indices.

Parameters:

Name	Type	Description	Default
`categorical_features`		list List of categorical feature indices.	required

classifier_as_regressor ¶