set_threshold

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

set_threshold function

<dl><dt>lnr</dt>
<dd>The learner to modify.</dd>
<dt>label</dt>
<dd>The referenced label.</dd>
<dt>threshold</dt>
<dd>The probability threshold above which <code>label</code> will be
be predicted.</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.set_threshold!'><code>IAI.set_threshold!</code></a>

For a binary classification problem, update the the predicted labels in the
leaves of the learner to predict a label only if the predicted probability is
at least the specified threshold. — set_threshold

<dl>

<dt>lnr</dt>
<dd>The learner to modify.</dd>


<dt>label</dt>
<dd>The referenced label.</dd>


<dt>threshold</dt>
<dd>The probability threshold above which <code>label</code> will be
be predicted.</dd>


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

</dl>

For a binary classification problem, update the the predicted labels in the
leaves of the learner to predict a label only if the predicted probability is
at least the specified threshold.

set_threshold: For a binary classification problem, update the the predicted labels in the leaves of the learner to predict a label only if the predicted probability is at least the specified threshold.

Description

Usage

Arguments

Examples