plotParetoFronts2D {TunePareto} | R Documentation |
Draws a classical Pareto front plot of 2 objectives of a parameter tuning. The first objective is on the x axis of the plot, and the second objective is on the y axis. Points on a Pareto front are connected by lines. Each Pareto front is drawn in a different color.
plotParetoFronts2D(tuneParetoResult, objectives, drawLabels = TRUE, drawBoundaries = TRUE, labelPos = 4, fitLabels=TRUE, cex.conf=0.5, lty.fronts=1, pch.fronts=8, col.fronts, ...)
tuneParetoResult |
An object of class |
objectives |
The names or indices of the two objectives to plot. Pareto-optimality is determined only on the basis of these two objectives. Optional if the parameter tuning has exactly two objectives. |
drawLabels |
If set to true, the descriptions of the configurations are printed next to the points in the plot. |
drawBoundaries |
If set to true, the upper or lower objective limits supplied in the |
labelPos |
The position of the configuration labels in the plot (if |
fitLabels |
If this parameter is true (and |
cex.conf |
The size of the configuration labels in the plots (if |
lty.fronts |
A vector of line types to use for the Pareto fronts. By default, straight lines are drawn for all fronts. |
pch.fronts |
A vector of symbols to use for points on the Pareto fronts. All points on the same front will have the same symbol. By default, an asterisk is used. |
col.fronts |
A vector of colors to use for the Pareto fronts. By default, a predefined set of colors is used. |
... |
Further graphical parameters to be passed to the |
This function does not have a return value.
tunePareto
, plotDominationGraph
# optimize the 'cost' parameter according # to CV error and CV Specificity on the 'iris' data set r <- tunePareto(data = iris[, -ncol(iris)], labels = iris[, ncol(iris)], classifier = tunePareto.svm(), cost=c(0.001,0.005,0.01,0.05,0.1,0.5,1,5,10,50), objectiveFunctions=list(cvError(10, 10), cvSpecificity(10, 10, caseClass="setosa"))) # plot the Pareto graph plotParetoFronts2D(r)