# evaluate_Weka_classifier: Model Statistics for R/Weka Classifiers

## Description

Compute model performance statistics for a fitted Weka classifier.

## Usage

evaluate_Weka_classifier(object, newdata = NULL, cost = NULL,
numFolds = 0, complexity = FALSE,
class = FALSE, seed = NULL, ...)

## Arguments

object

a `Weka_classifier`

object.

newdata

an optional data frame in which to look for variables
with which to evaluate. If omitted or `NULL`

, the training
instances are used.

cost

a square matrix of (mis)classification costs.

numFolds

the number of folds to use in cross-validation.

complexity

option to include entropy-based statistics.

class

option to include class statistics.

seed

optional seed for cross-validation.

…

further arguments passed to other methods (see details).

## Value

An object of class `Weka_classifier_evaluation`

, a list of the
following components:

stringcharacter, concatenation of the string representations
of the performance statistics.

detailsvector, base statistics, e.g., the percentage of
instances correctly classified, etc.

detailsComplexityvector, entropy-based statistics (if
selected).

detailsClassmatrix, class statistics, e.g., the true positive
rate, etc., for each level of the response variable (if selected).

confusionMatrixtable, cross-classification of true and
predicted classes.

## Details

The function computes and extracts a non-redundant set of performance
statistics that is suitable for model interpretation. By default the
statistics are computed on the training data.

Currently argument `…`

only supports the logical variable
`normalize`

which tells Weka to normalize the cost matrix so that
the cost of a correct classification is zero.

Note that if the class variable is numeric only a subset of the statistics
are available. Arguments `complexity`

and `class`

are then
not applicable and therefore ignored.

## References

I. H. Witten and E. Frank (2005).
*Data Mining: Practical Machine Learning Tools and Techniques*.
2nd Edition, Morgan Kaufmann, San Francisco.

## Examples

# NOT RUN {
## Use some example data.
w <- read.arff(system.file("arff","weather.nominal.arff",
package = "RWeka"))
## Identify a decision tree.
m <- J48(play~., data = w)
m
## Use 10 fold cross-validation.
e <- evaluate_Weka_classifier(m,
cost = matrix(c(0,2,1,0), ncol = 2),
numFolds = 10, complexity = TRUE,
seed = 123, class = TRUE)
e
summary(e)
e$details
# }