ebc_confusion

Evaluates the performance of binary classifiers.  Computes
confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR,
accuracy, F1, DOR, ..), and area under the curve.  Outputs are well
suited for nested dataframes.

Antoine Bichat

evabic

Evaluation of Binary Classifiers

ebc_confusion function

<dl><dt>detected</dt>
<dd>Vector of elements that are detected.</dd>
<dt>true</dt>
<dd>Vector of element that are supposed to be detected.</dd>
<dt>all</dt>
<dd>Vector of all elements.</dd>
<dt>m</dt>
<dd>Total number of elements.</dd>
<dt>prop</dt>
<dd>Logical, default to <code>FALSE</code>. Should the matrix sum to one?</dd></dl>

Arguments

Confusion matrix — ebc_confusion

<dl>

<dt>detected</dt>
<dd>Vector of elements that are detected.</dd>


<dt>true</dt>
<dd>Vector of element that are supposed to be detected.</dd>


<dt>all</dt>
<dd>Vector of all elements.</dd>


<dt>m</dt>
<dd>Total number of elements.</dd>


<dt>prop</dt>
<dd>Logical, default to <code>FALSE</code>. Should the matrix sum to one?</dd>

</dl>

ebc_confusion: Confusion matrix

Description

Usage

Value

Arguments

Details

Examples