Implements an ensemble of quapy.method.aggregative.ACC quantifiers, as used by
Pérez-Gállego et al., 2019.
Equivalent to:
+>>> ensembleFactory(learner, ACC, param_grid, optim, param_mod_sel, **kwargs)
+See ensembleFactory() for further details.
Implements an ensemble of quapy.method.aggregative.CC quantifiers, as used by
Pérez-Gállego et al., 2019.
Equivalent to:
+>>> ensembleFactory(learner, CC, param_grid, optim, param_mod_sel, **kwargs)
+See ensembleFactory() for further details.
Implements an ensemble of quapy.method.aggregative.EMQ quantifiers.
Equivalent to:
+>>> ensembleFactory(learner, EMQ, param_grid, optim, param_mod_sel, **kwargs)
+See ensembleFactory() for further details.
Implements an ensemble of quapy.method.aggregative.HDy quantifiers, as used by
Pérez-Gállego et al., 2019.
Equivalent to:
+>>> ensembleFactory(learner, HDy, param_grid, optim, param_mod_sel, **kwargs)
+See ensembleFactory() for further details.
Implements an ensemble of quapy.method.aggregative.PACC quantifiers.
Equivalent to:
+>>> ensembleFactory(learner, PACC, param_grid, optim, param_mod_sel, **kwargs)
+See ensembleFactory() for further details.
Example to instantiate an Ensemble based on quapy.method.aggregative.PACC
+in which the base members are optimized for quapy.error.mae() via
+quapy.model_selection.GridSearchQ. The ensemble follows the policy Accuracy based
+on quapy.error.mae() (the same measure being optimized),
+meaning that a static selection of members of the ensemble is made based on their performance
+in terms of this error.
>>> param_grid = {
+>>> 'C': np.logspace(-3,3,7),
+>>> 'class_weight': ['balanced', None]
+>>> }
+>>> param_mod_sel = {
+>>> 'sample_size': 500,
+>>> 'protocol': 'app'
+>>> }
+>>> common={
+>>> 'max_sample_size': 1000,
+>>> 'n_jobs': -1,
+>>> 'param_grid': param_grid,
+>>> 'param_mod_sel': param_mod_sel,
+>>> }
+>>>
+>>> ensembleFactory(LogisticRegression(), PACC, optim='mae', policy='mae', **common)
+