getSummaryResults: Obtain a set of descriptive statistics of the results of a learner

Description

This function provides a set of descriptive statistics for each evaluation metric that is estimated on an experimental comparison. These statistics are obtained for a particular learner, and for one of the prediction problems involved in the experimental comparison.

Usage

getSummaryResults(results,learner,dataSet)

Arguments

results

This is a compExp object (type "class?compExp" for details) that contains the results of an experimental comparison obtained through the experimentalComparison() function.

learner

This is the string that identifies the learner.

dataSet

The string that identifies the data set for which you want to get the scores.

Value

The function returns a matrix with the rows representing summary statistics of the scores obtained by the model on the different iterations, and the columns representing the evaluation statistics estimated in the experiment.

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 on  a data set, using one repetition of 10-fold CV
data(swiss)

## 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)
}

results <- experimentalComparison(
               c(dataset(Infant.Mortality ~ ., swiss)),
               c(variants('cv.rpartXse',se=c(0,0.5,1))),
               cvSettings(1,10,1234)
                                 )

## Get the statistics of a specific learner
getSummaryResults(results,'cv.rpartXse.v1','swiss')

## Get the statistics of the learner that obtained the best NMSE on the
## swiss data set
getSummaryResults(results,bestScores(results)$swiss['nmse','system'],'swiss')