predict_outcomes

The learner or grid to use for prediction.

For policy learners only, the reward matrix.

Julia Equivalent:
<a href="https://docs.interpretable.ai/v3.0.0/IAIBase/reference/#IAIBase-API-Reference-1"><code>IAI.predict_outcomes</code></a>
(for prescription or policy learners as appropriate)

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 

predict_outcomes function

<dl><dt>lnr</dt>
<dd>The learner or grid to use for prediction.</dd>
<dt>X</dt>
<dd>The features of the data.</dd>
<dt>...</dt>
<dd>For policy learners only, the reward matrix.</dd></dl>

Arguments

Requires IAI version 2.0 or higher for policy learners.

IAI Compatibility

Julia Equivalent:
<a href='https://docs.interpretable.ai/v3.0.0/IAIBase/reference/#IAIBase-API-Reference-1'><code>IAI.predict_outcomes</code></a>
(for prescription or policy learners as appropriate)

Return the predicted outcome for each treatment made by a model for each
point in the features — predict_outcomes

<dl>

<dt>lnr</dt>
<dd>The learner or grid to use for prediction.</dd>


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


<dt>...</dt>
<dd>For policy learners only, the reward matrix.</dd>

</dl>

Requires IAI version 2.0 or higher for policy learners.

predict_outcomes: Return the predicted outcome for each treatment made by a model for each point in the features

Description

Usage

Arguments

IAI Compatibility

Examples