score.numeric_reward_estimator

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

score.numeric_reward_estimator function

<dl><dt>obj</dt>
<dd>The learner or grid to evaluate.</dd>
<dt>X</dt>
<dd>The features of the data.</dd>
<dt>...</dt>
<dd>Other parameters, including zero or more target vectors as
required by the problem type. Refer to the Julia
documentation for other available parameters.</dd></dl>

Arguments

Requires IAI version 2.1 or higher.

IAI Compatibility

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

Calculate the scores for a numeric reward estimator on the given data — score.numeric_reward_estimator

<dl>

<dt>obj</dt>
<dd>The learner or grid to evaluate.</dd>


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


<dt>...</dt>
<dd>Other parameters, including zero or more target vectors as
required by the problem type. Refer to the Julia
documentation for other available parameters.</dd>

</dl>

Requires IAI version 2.1 or higher.

score.numeric_reward_estimator: Calculate the scores for a numeric reward estimator on the given data

Description

Usage

Arguments

IAI Compatibility

Examples