similarity_comparison

Refer to the
<a href="https://docs.interpretable.ai/v3.2.2/IAITrees/stability/#Tree-Stability-1">documentation on tree stability</a>
for more information.

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 

similarity_comparison function

<dl><dt>lnr</dt>
<dd>The original learner</dd>
<dt>new_lnr</dt>
<dd>The new learner</dd>
<dt>deviations</dt>
<dd>The deviation between the original tree and each tree in
the new learner</dd></dl>

Arguments

Requires IAI version 2.2 or higher.

IAI Compatibility

Refer to the
<a href='https://docs.interpretable.ai/v3.2.2/IAITrees/stability/#Tree-Stability-1'>documentation on tree stability</a>
for more information.

Conduct a similarity comparison between the final tree in a learner and
all trees in a new learner to consider the tradeoff between training
performance and similarity to the original tree — similarity_comparison

<dl>

<dt>lnr</dt>
<dd>The original learner</dd>


<dt>new_lnr</dt>
<dd>The new learner</dd>


<dt>deviations</dt>
<dd>The deviation between the original tree and each tree in
the new learner</dd>

</dl>

Requires IAI version 2.2 or higher.

Conduct a similarity comparison between the final tree in a learner and
all trees in a new learner to consider the tradeoff between training
performance and similarity to the original tree

similarity_comparison: Conduct a similarity comparison between the final tree in a learner and all trees in a new learner to consider the tradeoff between training performance and similarity to the original tree

Description

Usage

Arguments

IAI Compatibility

Details

Examples