score.supervised_learner

Julia Equivalent:
<a href="https://docs.interpretable.ai/v3.2.2/IAIBase/reference/#IAI.score-Tuple%7BSupervisedLearner%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.supervised_learner 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
available parameters.</dd></dl>

Arguments

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

Calculate the score for a model on the given data — score.supervised_learner

<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
available parameters.</dd>

</dl>

score.supervised_learner: Calculate the score for a model on the given data

Description

Usage

Arguments

Examples