prop_within

actual

predicted

Predictions in the closed interval from actual - tol to actual + tol count as within.

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

Calculates weighted proportion of predictions that are within a given tolerance around the actual values. The larger the value, 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

prop_within function

<dl><dt>actual</dt>
<dd>Observed values.</dd>
<dt>predicted</dt>
<dd>Predicted values.</dd>
<dt>w</dt>
<dd>Optional case weights.</dd>
<dt>tol</dt>
<dd>Predictions in the closed interval from actual - tol to actual + tol count as within.</dd>
<dt>...</dt>
<dd>Further arguments passed to <code>weighted_mean</code>.</dd></dl>

Arguments

Proportion Within — prop_within

<dl>

<dt>actual</dt>
<dd>Observed values.</dd>


<dt>predicted</dt>
<dd>Predicted values.</dd>


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


<dt>tol</dt>
<dd>Predictions in the closed interval from actual - tol to actual + tol count as within.</dd>


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

</dl>

prop_within: Proportion Within

Description

Usage

Value

Arguments

Examples