logLoss

Calculates weighted log loss (= cross-entropy).
Equals half of the unit Bernoulli deviance. The smaller, the better.

Provides weighted versions of several metrics and performance
measures used in machine learning, including average unit deviances of
the Bernoulli, Tweedie, Poisson, and Gamma distributions, see
Jorgensen B. (1997, ISBN: 978-0412997112).  The package also contains
a weighted version of generalized R-squared, see e.g. Cohen, J. et al.
(2002, ISBN: 978-0805822236).  Furthermore, 'dplyr' chains are
supported.

Michael Mayer

MetricsWeighted

Weighted Metrics and Performance Measures for Machine Learning

Christian Lorentzen

logLoss function

<dl><dt>actual</dt>
<dd>Observed values (0 or 1).</dd>
<dt>predicted</dt>
<dd>Predicted values strictly larger than 0 and smaller than 1.</dd>
<dt>w</dt>
<dd>Optional case weights.</dd>
<dt>...</dt>
<dd>Further arguments passed to <code>weighted_mean()</code>.</dd></dl>

Arguments

Log Loss/Binary Cross Entropy — logLoss

<dl>

<dt>actual</dt>
<dd>Observed values (0 or 1).</dd>


<dt>predicted</dt>
<dd>Predicted values strictly larger than 0 and smaller than 1.</dd>


<dt>w</dt>
<dd>Optional case weights.</dd>


<dt>...</dt>
<dd>Further arguments passed to <code>weighted_mean()</code>.</dd>

</dl>

logLoss: Log Loss/Binary Cross Entropy

Description

Usage

Value

Arguments

See Also

Examples