roc_curve.classification_learner

Julia Equivalent:
<a href="https://docs.interpretable.ai/v3.2.2/IAIBase/reference/#IAI.ROCCurve-Tuple%7BClassificationLearner%2C%20Union%7BDataFrames.AbstractDataFrame%2C%20AbstractMatrix%7B%3C%3AReal%7D%7D%2C%20AbstractVector%7D"><code>IAI.ROCCurve</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 

roc_curve.classification_learner function

<dl><dt>obj</dt>
<dd>The learner or grid to use for prediction.</dd>
<dt>X</dt>
<dd>The features of the data.</dd>
<dt>y</dt>
<dd>The labels of the data.</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/IAIBase/reference/#IAI.ROCCurve-Tuple%7BClassificationLearner%2C%20Union%7BDataFrames.AbstractDataFrame%2C%20AbstractMatrix%7B%3C%3AReal%7D%7D%2C%20AbstractVector%7D'><code>IAI.ROCCurve</code></a>

Construct an ROC curve using a trained classification learner on the given
data — roc_curve.classification_learner

<dl>

<dt>obj</dt>
<dd>The learner or grid to use for prediction.</dd>


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


<dt>y</dt>
<dd>The labels of the data.</dd>


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

</dl>

roc_curve.classification_learner: Construct an ROC curve using a trained classification learner on the given data

Description

Usage

Arguments

Examples