fit_predict.categorical_reward_estimator

Julia Equivalent:
<a href="https://docs.interpretable.ai/v3.2.2/RewardEstimation/reference/#IAI.fit_predict%21-Tuple%7BCategoricalRewardEstimator%7D"><code>IAI.fit_predict!</code></a>

An interface to the algorithms of 'Interpretable AI'
<https://www.interpretable.ai> from the R programming language.
'Interpretable AI' provides various modules, including 'Optimal Trees' for
classification, regression, prescription and survival analysis, 'Optimal
Imputation' for missing data imputation and outlier detection, and 'Optimal
Feature Selection' for exact sparse regression. The 'iai' package is an
open-source project. The 'Interpretable AI' software modules are proprietary
products, but free academic and evaluation licenses are available.

Jack Dunn

Interface to 'Interpretable AI' Modules

Ying Zhuo

Interpretable AI LLC 

fit_predict.categorical_reward_estimator function

<dl><dt>obj</dt>
<dd>The learner or grid to use for estimation</dd>
<dt>X</dt>
<dd>The features of the data.</dd>
<dt>treatments</dt>
<dd>The treatment applied to each point in the data.</dd>
<dt>...</dt>
<dd>Additional arguments depending on the treatment and outcome types.
Refer to the Julia documentation for more information.</dd></dl>

Arguments

Requires IAI version 2.0 or higher.

IAI Compatibility

Julia Equivalent:
<a href='https://docs.interpretable.ai/v3.2.2/RewardEstimation/reference/#IAI.fit_predict%21-Tuple%7BCategoricalRewardEstimator%7D'><code>IAI.fit_predict!</code></a>

Fit a categorical reward estimator on features, treatments and
outcomes and return predicted counterfactual rewards for each observation,
under each treatment observed in the data, as well as the scores of the
internal estimators. — fit_predict.categorical_reward_estimator

<dl>

<dt>obj</dt>
<dd>The learner or grid to use for estimation</dd>


<dt>X</dt>
<dd>The features of the data.</dd>


<dt>treatments</dt>
<dd>The treatment applied to each point in the data.</dd>


<dt>...</dt>
<dd>Additional arguments depending on the treatment and outcome types.
Refer to the Julia documentation for more information.</dd>

</dl>

Requires IAI version 2.0 or higher.

Fit a categorical reward estimator on features, treatments and
outcomes and return predicted counterfactual rewards for each observation,
under each treatment observed in the data, as well as the scores of the
internal estimators.

fit_predict.categorical_reward_estimator: Fit a categorical reward estimator on features, treatments and outcomes and return predicted counterfactual rewards for each observation, under each treatment observed in the data, as well as the scores of the internal estimators.

Description

Usage

Arguments

IAI Compatibility

Examples