fit_predict

Julia Equivalent:
<a href="https://docs.interpretable.ai/v3.2.2/RewardEstimation/reference/#IAI.fit_predict%21-Tuple%7BRewardEstimator%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 function

<dl><dt>obj</dt>
<dd>The object controlling which method is used</dd>
<dt>...</dt>
<dd>Arguments depending on the specific method used</dd></dl>

Arguments

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

Generic function for fitting a reward estimator on features,
treatments and returning predicted counterfactual rewards and scores of the
internal estimators. — fit_predict

<dl>

<dt>obj</dt>
<dd>The object controlling which method is used</dd>


<dt>...</dt>
<dd>Arguments depending on the specific method used</dd>

</dl>

Generic function for fitting a reward estimator on features,
treatments and returning predicted counterfactual rewards and scores of the
internal estimators.

fit_predict: Generic function for fitting a reward estimator on features, treatments and returning predicted counterfactual rewards and scores of the internal estimators.

Description

Usage

Arguments