prune_trees

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

prune_trees function

<dl><dt>lnr</dt>
<dd>The learner to prune</dd>
<dt>...</dt>
<dd>Refer to the Julia documentation for available parameters</dd></dl>

Arguments

Requires IAI version 3.0 or higher.

IAI Compatibility

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

Use the trained trees in a learner along with the supplied validation data to
determine the best value for the `cp` parameter and then prune the trees
according to this value — prune_trees

<dl>

<dt>lnr</dt>
<dd>The learner to prune</dd>


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

</dl>

Requires IAI version 3.0 or higher.

Use the trained trees in a learner along with the supplied validation data to
determine the best value for the `cp` parameter and then prune the trees
according to this value

prune_trees: Use the trained trees in a learner along with the supplied validation data to determine the best value for the `cp` parameter and then prune the trees according to this value

Description

Usage

Arguments

IAI Compatibility

Examples