BinaryQuantifier

class mlquantify.multiclass.BinaryQuantifier

Meta-quantifier enabling One-vs-Rest and One-vs-One strategies.

This class extends a base quantifier to handle multiclass problems by decomposing them into binary subproblems. It automatically delegates fitting, prediction, and aggregation operations to the appropriate binary quantifiers.

Attributes:

qtfs_dict

Dictionary mapping class labels or label pairs to fitted binary quantifiers.

strategy{‘ovr’, ‘ovo’}

Defines how multiclass quantification is decomposed.

aggregate(*args)

Aggregate binary predictions to obtain multiclass prevalence estimates.

fit(X, y)

Fit the quantifier under a binary decomposition strategy.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

predict(X)

Predict class prevalences using the trained binary quantifiers.

save_quantifier(path: str | None = None) → None

Save the quantifier instance to a file.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.