RWeka (version 0.3-11)

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.

References

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

Examples

Run this code
## 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

Run the code above in your browser using DataCamp Workspace