precision

actual

predicted

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

Calculates weighted precision, see <a href="https://en.wikipedia.org/wiki/Precision_and_recall">https://en.wikipedia.org/wiki/Precision_and_recall</a> for the (unweighted) version. The higher, 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

precision function

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

Arguments

Calculates weighted precision, see <a href='https://en.wikipedia.org/wiki/Precision_and_recall'>https://en.wikipedia.org/wiki/Precision_and_recall</a> for the (unweighted) version. The higher, the better.

Precision — precision

<dl>

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


<dt>predicted</dt>
<dd>Predicted values (0 or 1).</dd>


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


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

</dl>

precision: Precision

Description

Usage

Value

Arguments

See Also

Examples