classif_params

internal

Implements multiple performance measures for supervised
learning.  Includes over 40 measures for regression and
classification. Additionally, meta information about the performance
measures can be queried, e.g. what the best and worst possible
performances scores are.

Marc Becker

mlr3measures

Performance Measures for 'mlr3'

Michel Lang

Martin Binder

Lona Koers

classif_params function

<dl><dt>truth</dt>
<dd>(<code><a href="/link/factor()?package=mlr3measures&version=1.0.0" data-mini-rdoc="mlr3measures::factor()">factor()</a></code>) 
True (observed) labels.
Must have the same levels and length as <code>response</code>.</dd>
<dt>response</dt>
<dd>(<code><a href="/link/factor()?package=mlr3measures&version=1.0.0" data-mini-rdoc="mlr3measures::factor()">factor()</a></code>) 
Predicted response labels.
Must have the same levels and length as <code>truth</code>.</dd>
<dt>prob</dt>
<dd>(<code><a href="/link/matrix()?package=mlr3measures&version=1.0.0" data-mini-rdoc="mlr3measures::matrix()">matrix()</a></code>) 
Matrix of predicted probabilities, each column is a vector of probabilities for a
specific class label.
Columns must be named with levels of <code>truth</code>.</dd>
<dt>sample_weights</dt>
<dd>(<code><a href="/link/numeric()?package=mlr3measures&version=1.0.0" data-mini-rdoc="mlr3measures::numeric()">numeric()</a></code>) 
Vector of non-negative and finite sample weights.
Must have the same length as <code>truth</code>.
The vector gets automatically normalized to sum to one.
Defaults to equal sample weights.</dd>
<dt>na_value</dt>
<dd>(<code>numeric(1)</code>) 
Value that should be returned if the measure is not defined for the input
(as described in the note). Default is <code>NaN</code>.</dd>
<dt>...</dt>
<dd>(<code>any</code>) 
Additional arguments. Currently ignored.</dd></dl>

Arguments

Classification Parameters — classif_params

<dl>

<dt>truth</dt>
<dd>(<code><a href='https://rdrr.io/r/base/factor.html'>factor()</a></code>) 
True (observed) labels.
Must have the same levels and length as <code>response</code>.</dd>


<dt>response</dt>
<dd>(<code><a href='https://rdrr.io/r/base/factor.html'>factor()</a></code>) 
Predicted response labels.
Must have the same levels and length as <code>truth</code>.</dd>


<dt>prob</dt>
<dd>(<code><a href='https://rdrr.io/r/base/matrix.html'>matrix()</a></code>) 
Matrix of predicted probabilities, each column is a vector of probabilities for a
specific class label.
Columns must be named with levels of <code>truth</code>.</dd>


<dt>sample_weights</dt>
<dd>(<code><a href='https://rdrr.io/r/base/numeric.html'>numeric()</a></code>) 
Vector of non-negative and finite sample weights.
Must have the same length as <code>truth</code>.
The vector gets automatically normalized to sum to one.
Defaults to equal sample weights.</dd>


<dt>na_value</dt>
<dd>(<code>numeric(1)</code>) 
Value that should be returned if the measure is not defined for the input
(as described in the note). Default is <code>NaN</code>.</dd>


<dt>...</dt>
<dd>(<code>any</code>) 
Additional arguments. Currently ignored.</dd>

</dl>

Classification Parameters

classif_params: Classification Parameters

Description

Arguments