ContextualBanditModelSelection

Contextual Bandit-based model selection.

Similar to BanditModelSelection, but now the algorithm uses a contextual bandit leading to more fine-grained model selection.

Parameters

contextualbandit(Default:LinTS) → The underlying bandit algorithm. Options are: LinTS, and LinUCB.
metric_name(Default:WindowedMAE) → The metric to use to evaluate models. Options are: WindowedAUC, WindowedAccuracy, WindowedMAE, WindowedMSE, and WindowedRMSE.
base_models(list[Model]) → The list of models over which to perform model selection.

Example Usage

We can create an instance and deploy BanditModel like this.

import turboml as tb
htc_model = tb.HoeffdingTreeRegressor()
amf_model = tb.AMFRegressor()
ffm_model = tb.FFMRegressor()
bandit_model = tb.ContextualBanditModelSelection(base_models = [htc_model, amf_model, ffm_model])

Bandit Model Selection Leveraging Bagging Classifier