ks_cluster_assignment_step

Function to assign classes to clusters based on Kolmogorov-Smirnov clustering

Provides tools for estimating model-agnostic prediction intervals using conformal prediction, bootstrapping, and parametric prediction intervals. The package is designed for ease of use, offering intuitive functions for both binned and full conformal prediction methods, as well as parametric interval estimation with diagnostic checks. Currently only working for continuous predictions. For details on the conformal and bin-conditional conformal prediction methods, see Randahl, Williams, and Hegre (2024) <DOI:10.48550/arXiv.2410.14507>.

David Randahl

pintervals

Model Agnostic Prediction Intervals

Jonathan P. Williams

Anders Hjort

ks_cluster_assignment_step function

<dl><dt>ncs</dt>
<dd>Vector of non-conformity scores</dd>
<dt>class_vec</dt>
<dd>Vector of class labels</dd>
<dt>class_labels</dt>
<dd>Vector of unique class labels</dd>
<dt>clusters</dt>
<dd>List of clusters</dd>
<dt>m</dt>
<dd>Number of clusters</dd></dl>

Arguments

Function to assign classes to clusters based on Kolmogorov-Smirnov clustering — ks_cluster_assignment_step

<dl>

<dt>ncs</dt>
<dd>Vector of non-conformity scores</dd>


<dt>class_vec</dt>
<dd>Vector of class labels</dd>


<dt>class_labels</dt>
<dd>Vector of unique class labels</dd>


<dt>clusters</dt>
<dd>List of clusters</dd>


<dt>m</dt>
<dd>Number of clusters</dd>

</dl>

ks_cluster_assignment_step: Function to assign classes to clusters based on Kolmogorov-Smirnov clustering

Description

Usage

Value

Arguments