roc_curve.default

Julia Equivalent:
<a href="https://docs.interpretable.ai/v3.2.2/IAIBase/reference/#IAI.ROCCurve-Tuple%7BAbstractVector%7B%3C%3AReal%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.default function

<dl><dt>obj</dt>
<dd>The predicted probabilities for each point in the data.</dd>
<dt>y</dt>
<dd>The true labels of the data.</dd>
<dt>positive_label</dt>
<dd>The label for which probability is being predicted.</dd>
<dt>...</dt>
<dd>Additional arguments (unused)</dd></dl>

Arguments

Requires IAI version 2.0 or higher.

IAI Compatibility

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

Construct an ROC curve from predicted probabilities and true labels — roc_curve.default

<dl>

<dt>obj</dt>
<dd>The predicted probabilities for each point in the data.</dd>


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


<dt>positive_label</dt>
<dd>The label for which probability is being predicted.</dd>


<dt>...</dt>
<dd>Additional arguments (unused)</dd>

</dl>

Requires IAI version 2.0 or higher.

roc_curve.default: Construct an ROC curve from predicted probabilities and true labels

Description

Usage

Arguments

IAI Compatibility

Examples