MLPE

class mlquantify.likelihood.MLPE(learner=None)

Maximum Likelihood Prevalence Estimation (MLPE).

Returns training priors as prevalence estimates without adaptations.

Parameters:

learnerestimator, optional

Base classifier.

References

[2]

Esuli, A., Moreo, A., & Sebastiani, F. (2023). Learning to Quantify. Springer.

fit(X, y)

Fit the quantifier using the provided data and learner.

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 for the given data.

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.