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 0.4-1 Index]