logLoss

actual

Predicted values strictly larger than 0 and smaller than 1.

predicted

Further arguments passed to <code>weighted_mean</code>.

Calculates weighted logloss resp. cross entropy. Equals half of the unit Bernoulli deviance. The smaller, the better.

Provides weighted versions of several metrics, scoring
functions 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, Scoring Functions 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