# accuracy

##### Prediction Accuracy from Stability Assessment Results

Function to compute the prediction accuracy from an object
of class `"stablelearner"`

or `"stablelearnerList"`

as a parallel
to the similarity values estimated by `stability`

in each
iteration of the stability assessment procedure.

- Keywords
- resampling, similarity

##### Usage

```
accuracy(x, measure = "kappa", na.action = na.exclude,
applyfun = NULL, cores = NULL)
```

##### Arguments

- x
an object of class

`"stablelearner"`

or`"stablelearnerList"`

.- measure
a character string (or a vector of character strings). Name(s) of the measure(s) used to compute accuracy. Currently implemented measures are

`"diag"`

= percentage of observations on the main diagonal of a confusion matrix,`"kappa"`

=`"diag"`

corrected for agreement by chance (default),`"rand"`

= Rand index, and`"crand"`

= Rand index corrected for agreemend by chance (see also`classAgreement`

).- na.action
a function which indicates what should happen to the predictions of the results containing

`NAs`

. The default function is`na.exclude`

.- applyfun
a

`lapply`

-like function. The default is to use`lapply`

unless`cores`

is specified in which case`mclapply`

is used (for multicore computations on platforms that support these).- cores
integer. The number of cores to use in multicore computations using

`mclapply`

(see above).

##### Details

This function can be used to compute prediction accuracy after the stability was
estimated using `stability`

.

##### Value

A matrix of size `2*B`

times length(`measure`

) containing prediction
accuracy values of the learners trained during the stability assessment procedure.

##### See Also

##### Examples

```
# NOT RUN {
# }
# NOT RUN {
library("partykit")
res <- ctree(Species ~ ., data = iris)
stab <- stability(res)
accuracy(stab)
# }
# NOT RUN {
# }
```

*Documentation reproduced from package stablelearner, version 0.1-2, License: GPL-2 | GPL-3*