statScores: Obtains a summary statistic of one of the evaluation metrics used in an experimental comparison, for all learners and data sets involved in the comparison.

Description

Given a compExp object this function provides a summary statistic (defaulting to the average score) of the different scores obtained on a single evaluation statistic over all repetitions carried out in the experimental process. This is done for all learners and data sets of the experimental comparison. The function can be handy to obtain things like for instance the maximum score obtained by each learner on a particular statistic over all repetitions of the experimental process.

Usage

statScores(compRes, stat, summary = "mean")

Arguments

compRes

An object of class compExp with the results of the experimental comparison.

stat

A string with the name of the evaluation metric for which you want to obtain the scores.

summary

A string with the name of the function that should be used to aggregate the different repetition results into a single score (defaults to the mean value).

Value

The result of this function is a named list with as many components as there are data sets in the evaluation comparison being used. For each data set (component), we get a named vector with as many elements as there are learners in the experiment. The value for each learner is the result of applying the aggregation function (parameter summary) to the different scores obtained by the learner on the evaluation metric specified by the parameter stat.

References

Torgo, L. (2010) Data Mining using R: learning with case studies, CRC Press (ISBN: 9781439810187).

http://www.dcc.fc.up.pt/~ltorgo/DataMiningWithR

Examples

Run this code

## Estimating several evaluation metrics on different variants of a
## regression tree and of a SVM, on  two data sets, using one repetition
## of  10-fold CV
data(swiss)
data(mtcars)

## First the user defined functions 
cv.rpartXse <- function(form, train, test, ...) {
    require(DMwR)
    t <- rpartXse(form, train, ...)
    p <- predict(t, test)
    mse <- mean((p - resp(form, test))^2)
    c(nmse = mse/mean((mean(resp(form, train)) - resp(form, test))^2), 
        mse = mse)
}

## run the experimental comparison
results <- experimentalComparison(
               c(dataset(Infant.Mortality ~ ., swiss),
                 dataset(mpg ~ ., mtcars)),
               c(variants('cv.rpartXse',se=c(0,0.5,1))),
               cvSettings(1,10,1234)
                                 )

## Get the maximum value of nmse for each learner
statScores(results,'nmse','max')
## Get the interquartile range of the mse score for each learner
statScores(results,'mse','IQR')

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