precision | R Documentation |
Precision calculations
precision(x, ...)
## S3 method for class 'accuracy'
precision(x, ...)
x |
an object from which precision is to be computed |
... |
generic functionality, not used |
output depends on input and meaning of the function (the term precision
is highly polysemic)
accuracy
: Compute precision and goodness for accuracy curves, after Deutsch (1997),
using the accuracy curve obtained with accuracy()
. This returns a named vector with
two values, one for precision
and one for goodness
.
Mean accuracy, precision and goodness were defined by Deutsch (1997)
for an accuracy curve \{(p_i, \pi_i), i=1,2, \ldots, I\}
, where \{p_i\}
are a sequence of nominal confidence of prediction intervals and each \pi_i
is the actual coverage of an interval with nominal confidence p_i
.
Out of these values, the mean accuracy (see mean.accuracy()
) is computed as
A = \int_{0}^{1} I\{(\pi_i-p_i)>0\} dp,
where the indicator I\{(\pi_i-p_i)>0\}
is 1 if the condition is satisfied and
0 otherwise. Out of it, the area above the 1:1 bisector and under the accuracy
curve is the precision
P = 1-2\int_{0}^{1} (\pi_i-p_i)\cdot I\{(\pi_i-p_i)>0\} dp,
which only takes into account those points of the accuracy curve where \pi_i>p_i
.
To consider the whole curve, goodness can be used
G = 1-\int_{0}^{1} (\pi_i-p_i)\cdot (3\cdot I\{(\pi_i-p_i)>0\}-2) dp.
Other accuracy functions:
accuracy()
,
mean.accuracy()
,
plot.accuracy()
,
validate()
,
xvErrorMeasures.default()
,
xvErrorMeasures()
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