predict.supervised_learner

Julia Equivalent:
<a href="https://docs.interpretable.ai/v3.2.2/IAIBase/reference/#IAI.predict-Tuple%7BSupervisedLearner%2C%20Union%7BDataFrames.AbstractDataFrame%2C%20AbstractMatrix%7B%3C%3AReal%7D%7D%7D"><code>IAI.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 

predict.supervised_learner function

<dl><dt>obj</dt>
<dd>The learner or grid to use for prediction.</dd>
<dt>X</dt>
<dd>The features of the data.</dd>
<dt>...</dt>
<dd>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.predict-Tuple%7BSupervisedLearner%2C%20Union%7BDataFrames.AbstractDataFrame%2C%20AbstractMatrix%7B%3C%3AReal%7D%7D%7D'><code>IAI.predict</code></a>

Return the predictions made by a supervised learner for each point in the
features — predict.supervised_learner

<dl>

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


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


<dt>...</dt>
<dd>Refer to the Julia documentation for available parameters.</dd>

</dl>

predict.supervised_learner: Return the predictions made by a supervised learner for each point in the features

Description

Usage

Arguments

Examples