EvalRegressMetrics: Utility metrics for assessing the performance of...

In paobranco/UBL: An Implementation of Re-Sampling Approaches to Utility-Based Learning for Both Classification and Regression Tasks

Description

This function allows to evaluate utility-based metrics in regression problems which have defined a cost, benefit, or utility surface.

Usage

EvalRegressMetrics(trues, preds, util.vals, type = "util",
metrics = NULL, thr = 0.5, control.parms = NULL, 
beta = 1, maxC = NULL, maxB = NULL)

Arguments

trues	A vector with the true target variable values of the problem.
preds	A vector with the prediction values obtained for the vector of trues.
util.vals	Either the cost, benefit or utility values corresponding to the provided points (trues, preds).
type	A character specifying the type of surface under consideration. Can be set to "cost", "benefit" or "utility" (the default).
metrics	A character vector with the metrics names to be evaluated. If not specified (the default), all the metrics avaliable for the type of surface provided are evaluated.
thr	A numeric value between 0 and 1 setting a threshold on the relevance values for determining which are the important cases to consider. This threshold is only necessary for the following metrics: precPhi, recPhi and FPhi. Moreover, these metrics are only available for problems based on utility surfaces. Defaults to 0.5.
control.parms	the control.parms of the relevance function phi. Can be obtained through function phi.control. These are only necessary for evaluating the following utility metrics: recPhi, precPhi and FPhi.
beta	The numeric value of the beta parameter for F-score.
maxC	the maximum cost achievable in the cost surface. Parameter only required when the problem depends on a cost surface.
maxB	the maximum Benefit achievable in the benefit surface. Parameter only required when the problem depends on a benefit surface.

Value

The function returns a named list with the evaluated metrics results.

Author(s)

Paula Branco [e-mail], Rita Ribeiro [e-mail] and Luis Torgo [e-mail]

References

Ribeiro, R., 2011. Utility-based regression (Doctoral dissertation, PhD thesis, Dep. Computer Science, Faculty of Sciences - University of Porto).

Branco, P., 2014. Re-sampling Approaches for Regression Tasks under Imbalanced Domains (Msc thesis, Dep. Computer Science, Faculty of Sciences - University of Porto).

See Also

phi.control

Examples

## Not run: 
#Example using a utility surface interpolated and observing the performance of 
# two models: i) a model obtained with a strategy designed for maximizing 
# predictions utility and a model obtained through a standard random Forest.

data(Boston, package = "MASS")

tgt <- which(colnames(Boston) == "medv")
sp <- sample(1:nrow(Boston), as.integer(0.7*nrow(Boston)))
train <- Boston[sp,]
test <- Boston[-sp,]

control.parms <- phi.control(Boston[,tgt], method="extremes", extr.type="both")
# the boundaries of the domain considered
minds <- min(train[,tgt])
maxds <- max(train[,tgt])

# build m.pts to include at least (minds, maxds) and (maxds, minds) points
# m.pts must only contain points in [minds, maxds] range.
m.pts <- matrix(c(minds, maxds, -1, maxds, minds, -1),
                byrow=TRUE, ncol=3)

pred.res <- UtilOptimRegress(medv~., train, test, type = "util", strat = "interpol",
                             strat.parms=list(method = "bilinear"),
                             control.parms = control.parms,
                             m.pts = m.pts, minds = minds, maxds = maxds)

# assess the performance
eval.util <- EvalRegressMetrics(test$medv, pred.res$optim, pred.res$utilRes,
                                thr = 0.8, control.parms = control.parms)

# now train a normal model
model <- randomForest(medv~.,train)
normal.preds <- predict(model, test)

#obtain the utility of this model predictions
NormalUtil <- UtilInterpol(test$medv, normal.preds, type = "util",
                           control.parms = control.parms,
                           minds, maxds, m.pts, method = "bilinear")

#check the performance
eval.normal <- EvalRegressMetrics(test$medv, normal.preds, NormalUtil,
                                  thr=0.8, control.parms = control.parms)

# 3 check visually the utility surface and the predictions of both models 
UtilInterpol(NULL,NULL, type = "util", control.parms = control.parms,
                           minds, maxds, m.pts, method = "bilinear", 
                           visual=TRUE, full.output = TRUE)
points(test$medv, normal.preds) # standard model predition points
points(test$medv, pred.res$optim, col="blue") # model with optimized predictions

## End(Not run)

paobranco/UBL documentation built on May 6, 2021, 6:57 p.m.