classif_params

:: <code>factor()</code>
True (observed) labels.
Must have the same levels and length as <code>response</code>.

truth

:: <code>factor()</code>
Predicted response labels.
Must have the same levels and length as <code>truth</code>.

response

:: <code>matrix()</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>.

prob

:: <code>numeric()</code>
Non-negative sample weights.
Must have the same levels and length as <code>truth</code>.
Defaults to equal sample weights.

sample_weights

:: <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>.

na_value

:: <code>any</code>
Additional arguments. Currently ignored.

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.

classif_params: Classification Parameters

Description

Arguments