libuplift.meta.multi_model ========================== .. py:module:: libuplift.meta.multi_model .. autoapi-nested-parse:: Uplift models based on multiple classification/regression models. .. !! processed by numpydoc !! Classes ------- .. autoapisummary:: libuplift.meta.multi_model.MultimodelUpliftRegressor libuplift.meta.multi_model.TLearnerUpliftRegressor libuplift.meta.multi_model.MultimodelUpliftClassifier libuplift.meta.multi_model.TLearnerUpliftClassifier libuplift.meta.multi_model.MultimodelUpliftLinearRegressor libuplift.meta.multi_model.MultimodelUpliftLinearRegressorJamesSeparate libuplift.meta.multi_model.MultimodelUpliftLinearRegressorJamesU libuplift.meta.multi_model.MultimodelUpliftLinearRegressorMSESeparate libuplift.meta.multi_model.MultimodelUpliftLinearRegressorMSEU Module Contents --------------- .. py:class:: MultimodelUpliftRegressor(base_estimator=LinearRegression(), ignore_control=False) Bases: :py:obj:`libuplift.base.UpliftRegressorMixin`, :py:obj:`_MultimodelUpliftModel` Multimodel uplift regressor. Build separate models for control and all treatments, subtract control predictions from treatment predictions. :Parameters: **base_estimator** : a sklearn regressor or list of (name, regressor) tuples. If a list is provided the first model is used for control, successive one for treatments. If different parameters are to be used per each model, the list version must be given. If a single estimator is given it will be cloned for every treatment. .. !! processed by numpydoc !! .. py:method:: predict(X) .. py:class:: TLearnerUpliftRegressor(base_estimator=LinearRegression(), ignore_control=False) Bases: :py:obj:`MultimodelUpliftRegressor` Multimodel uplift regressor. Build separate models for control and all treatments, subtract control predictions from treatment predictions. :Parameters: **base_estimator** : a sklearn regressor or list of (name, regressor) tuples. If a list is provided the first model is used for control, successive one for treatments. If different parameters are to be used per each model, the list version must be given. If a single estimator is given it will be cloned for every treatment. .. !! processed by numpydoc !! .. py:class:: MultimodelUpliftClassifier(base_estimator=LogisticRegression()) Bases: :py:obj:`_MultimodelUpliftClassifierBase` Multimodel uplift classifier. Build separate models for control and all treatments, subtract control predicted probs from treatment predicted probs. :Parameters: **base_estimator** : a sklearn classifier supporting predict_proba or list of (name, regressor) tuples. If a list is provided the first model is used for control, successive one for treatments. If different parameters are to be used per each model, the list version must be given. If a single estimator is given it will be cloned for every treatment. .. !! processed by numpydoc !! .. py:class:: TLearnerUpliftClassifier(base_estimator=LogisticRegression()) Bases: :py:obj:`MultimodelUpliftClassifier` Multimodel uplift classifier. Build separate models for control and all treatments, subtract control predicted probs from treatment predicted probs. :Parameters: **base_estimator** : a sklearn classifier supporting predict_proba or list of (name, regressor) tuples. If a list is provided the first model is used for control, successive one for treatments. If different parameters are to be used per each model, the list version must be given. If a single estimator is given it will be cloned for every treatment. .. !! processed by numpydoc !! .. py:class:: MultimodelUpliftLinearRegressor(base_estimator=LinearRegression(), ignore_control=False) Bases: :py:obj:`MultimodelUpliftRegressor`, :py:obj:`sklearn.linear_model._base.LinearModel` Uplift regressor with ``coef_`` and ``intercept_`` fields. .. !! processed by numpydoc !! .. py:method:: fit(*args, **kwargs) .. py:method:: predict(X) .. py:class:: MultimodelUpliftLinearRegressorJamesSeparate(base_estimator=LinearRegression(), ignore_control=False) Bases: :py:obj:`MultimodelUpliftRegressor`, :py:obj:`sklearn.linear_model._base.LinearModel` Multimodel uplift regressor. Build separate models for control and all treatments, subtract control predictions from treatment predictions. :Parameters: **base_estimator** : a sklearn regressor or list of (name, regressor) tuples. If a list is provided the first model is used for control, successive one for treatments. If different parameters are to be used per each model, the list version must be given. If a single estimator is given it will be cloned for every treatment. .. !! processed by numpydoc !! .. py:method:: fit(*args, **kwargs) .. py:method:: predict(X) .. py:class:: MultimodelUpliftLinearRegressorJamesU(base_estimator=LinearRegression(), ignore_control=False) Bases: :py:obj:`MultimodelUpliftRegressor`, :py:obj:`sklearn.linear_model._base.LinearModel` Multimodel uplift regressor. Build separate models for control and all treatments, subtract control predictions from treatment predictions. :Parameters: **base_estimator** : a sklearn regressor or list of (name, regressor) tuples. If a list is provided the first model is used for control, successive one for treatments. If different parameters are to be used per each model, the list version must be given. If a single estimator is given it will be cloned for every treatment. .. !! processed by numpydoc !! .. py:method:: fit(*args, **kwargs) .. py:method:: predict(X) .. py:class:: MultimodelUpliftLinearRegressorMSESeparate(base_estimator=LinearRegression(), ignore_control=False) Bases: :py:obj:`MultimodelUpliftRegressor`, :py:obj:`sklearn.linear_model._base.LinearModel` Multimodel uplift regressor. Build separate models for control and all treatments, subtract control predictions from treatment predictions. :Parameters: **base_estimator** : a sklearn regressor or list of (name, regressor) tuples. If a list is provided the first model is used for control, successive one for treatments. If different parameters are to be used per each model, the list version must be given. If a single estimator is given it will be cloned for every treatment. .. !! processed by numpydoc !! .. py:method:: fit(*args, **kwargs) .. py:method:: predict(X) .. py:class:: MultimodelUpliftLinearRegressorMSEU(base_estimator=LinearRegression(), ignore_control=False) Bases: :py:obj:`MultimodelUpliftRegressor`, :py:obj:`sklearn.linear_model._base.LinearModel` Multimodel uplift regressor. Build separate models for control and all treatments, subtract control predictions from treatment predictions. :Parameters: **base_estimator** : a sklearn regressor or list of (name, regressor) tuples. If a list is provided the first model is used for control, successive one for treatments. If different parameters are to be used per each model, the list version must be given. If a single estimator is given it will be cloned for every treatment. .. !! processed by numpydoc !! .. py:method:: fit(*args, **kwargs) .. py:method:: predict(X)