variable_importance.tree_learner

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

variable_importance.tree_learner function

<dl><dt>obj</dt>
<dd>The learner to query.</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/IAITrees/reference/#IAI.variable_importance-Tuple%7BTreeLearner%7D'><code>IAI.variable_importance</code></a>

Generate a ranking of the variables in a tree learner according to their
importance during training. The results are normalized so that they sum to
one. — variable_importance.tree_learner

<dl>

<dt>obj</dt>
<dd>The learner to query.</dd>


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

</dl>

Generate a ranking of the variables in a tree learner according to their
importance during training. The results are normalized so that they sum to
one.

variable_importance.tree_learner: Generate a ranking of the variables in a tree learner according to their importance during training. The results are normalized so that they sum to one.

Description

Usage

Arguments

Examples