evaluate_Weka_classifier {RWeka}R Documentation

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).

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.

Value

An object of class Weka_classifier_evaluation, a list of the following components:

string character, concatenation of the string representations of the performance statistics.
details vector, base statistics, e.g., the percentage of instances correctly classified, etc.
detailsComplexity vector, entropy-based statistics (if selected).
detailsClass matrix, class statistics, e.g., the true positive rate, etc., for each level of the response variable (if selected).
confusionMatrix table, cross-classification of true and predicted classes.

References

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

Examples

## 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

[Package RWeka version 0.3-16 Index]