bord_dist

Calculation of the distance of each sample to the SVM decision boundary in a classification problem.

internal

Implementation of models for the controlled introduction of errors in
classification datasets. This package contains the noise models described in
Saez (2022) <doi:10.3390/math10203736> that allow corrupting class labels,
attributes and both simultaneously.

Jos<c3><a9> A. S<c3><a1>ez

noisemodel

Noise Models for Classification Datasets

José A. Sáez

bord_dist function

<dl><dt>x</dt>
<dd>a data frame of input attributes.</dd>
<dt>y</dt>
<dd>a factor vector with the output class of each sample.</dd>
<dt>krn</dt>
<dd>a character with the kernel of SVM -see <code><a href="/link/e1071%3A%3Asvm?package=noisemodel&version=1.0.2" data-mini-rdoc="noisemodel::e1071::svm">e1071::svm</a></code> (default: "<code>linear</code>").</dd></dl>

Arguments

Distance to SVM decision boundary — bord_dist

<dl>

<dt>x</dt>
<dd>a data frame of input attributes.</dd>


<dt>y</dt>
<dd>a factor vector with the output class of each sample.</dd>


<dt>krn</dt>
<dd>a character with the kernel of SVM -see <code><a href='https://rdrr.io/pkg/e1071/man/svm.html'>e1071::svm</a></code> (default: "<code>linear</code>").</dd>

</dl>

bord_dist: Distance to SVM decision boundary

Description

Usage

Value

Arguments