decision_path

Julia Equivalent:
<a href="https://docs.interpretable.ai/v3.2.2/IAITrees/reference/#IAI.decision_path"><code>IAI.decision_path</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 

decision_path function

<dl><dt>lnr</dt>
<dd>The learner or grid to query.</dd>
<dt>X</dt>
<dd>The features of the data.</dd></dl>

Arguments

Julia Equivalent:
<a href='https://docs.interpretable.ai/v3.2.2/IAITrees/reference/#IAI.decision_path'><code>IAI.decision_path</code></a>

Return a matrix where entry (i, j) is true if the ith point in
the features passes through the jth node in a trained tree model. — decision_path

<dl>

<dt>lnr</dt>
<dd>The learner or grid to query.</dd>


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

</dl>

Return a matrix where entry <code>(i, j)</code> is true if the <code>i</code>th point in
the features passes through the <code>j</code>th node in a trained tree model.

decision_path: Return a matrix where entry `(i, j)` is true if the `i`th point in the features passes through the `j`th node in a trained tree model.

Description

Usage

Arguments

Examples