evaluatePrediction: Evaluate Prediction
In kebabs: Kernel-Based Analysis Of Biological Sequences

Description Usage Arguments Details Value Author(s) References See Also Examples

Evaluate performance results of prediction on a testset based on given labels for binary classification

1
2
3

evaluatePrediction(prediction, label, allLabels = NULL, decValues = NULL,
  print = TRUE, confmatrix = TRUE, numPrecision = 3,
  numPosNegTrainSamples = numeric(0))

`prediction`	prediction results as returned by `predict` for predictionType="response".
`label`	label vector of same length as parameter 'prediction'.
`allLabels`	vector containing all occuring labels once. This parameter is required only if the label vector is numeric. Default=NULL
`decValues`	numeric vector containing decision values for the predictions as returned by the `predict` method with `predictionType` set to `decision`. This parameter is needed for the determination of the AUC value which is currently only supported for binary classification. Default=NULL
`print`	This parameter indicates whether performance values should be printed or returned as data frame without printing (for details see below). Default=TRUE
`confmatrix`	When set to TRUE a confusion matrix is printed. The rows correspond to predictions, the columns to the true labels. Default=TRUE
`numPrecision`	minimum number of digits to the right of the decimal point. Values between 0 and 20 are allowed. Default=3
`numPosNegTrainSamples`	optional integer vector with two values giving the number of positive and negative training samples. When this parameter is set the balancedness of the training set is reported. Default=numeric(0)

For binary classfication this function computes the performance measures accuracy, balanced accuracy, sensitivity, specificity, precision and the Matthews Correlation Coefficient(MCC). If decision values are passed in the parameter decValues the function additionally determines the AUC. When the number of positive and negative training samples is passed to the function it also shows the balancedness of the training set. The performance results are either printed by the routine directly or returned in a data frame. The columns of the data frame are:

column name	performance measure
--------------------	--------------
TP	true positive
FP	false positive
FN	false negative
TN	true negative
ACC	accuracy
BAL_ACC	balanced accuracy
SENS	sensitivity
SPEC	specificity
PREC	precision
MAT_CC	Matthews correlation coefficient
AUC	area under ROC curve
PBAL	prediction balancedness (fraction of positive samples)
TBAL	training balancedness (fraction of positive samples)

When the parameter 'print' is set to FALSE the function returns a data frame containing the prediction performance values (for details see above).

Johannes Palme <kebabs@bioinf.jku.at>

http://www.bioinf.jku.at/software/kebabs

J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package for kernel-based analysis of biological sequences. Bioinformatics, 31(15):2574-2576, 2015. DOI: 10.1093/bioinformatics/btv176.

predict, kbsvm

## set seed for random generator, included here only to make results
## reproducable for this example
set.seed(456)
## load transcription factor binding site data
data(TFBS)
enhancerFB
## select 70% of the samples for training and the rest for test
train <- sample(1:length(enhancerFB), length(enhancerFB) * 0.7)
test <- c(1:length(enhancerFB))[-train]
## create the kernel object for gappy pair kernel with normalization
gappy <- gappyPairKernel(k=1, m=3)
## show details of kernel object
gappy

## run training with explicit representation
model <- kbsvm(x=enhancerFB[train], y=yFB[train], kernel=gappy,
               pkg="LiblineaR", svm="C-svc", cost=80, explicit="yes",
               featureWeights="no")

## predict the test sequences
pred <- predict(model, enhancerFB[test])

## print prediction performance
evaluatePrediction(pred, yFB[test], allLabels=unique(yFB))

## Not run: 
## print prediction performance including AUC
## additionally determine decision values
preddec <- predict(model, enhancerFB[test], predictionType="decision")
evaluatePrediction(pred, yFB[test], allLabels=unique(yFB),
                   decValues=preddec)

## print prediction performance including training set balance
trainPosNeg <- c(length(which(yFB[train] == 1)),
                 length(which(yFB[train] == -1)))
evaluatePrediction(pred, yFB[test], allLabels=unique(yFB),
                   numPosNegTrainSamples=trainPosNeg)

## or get prediction performance as data frame
perf <- evaluatePrediction(pred, yFB[test], allLabels=unique(yFB),
                           print=FALSE)

## show performance values in data frame
perf

## End(Not run)