Description Usage Arguments Value See Also Examples
The cfPredict
function uses a classification ensemble object created by cfBuild
to predict the class(es) of one or more samples described by a given data matrix. The function returns the predicted classes for each sample, together with a confidence score (between 0 and 100) which equates to the percentage of SVMs within the classifier that voted for the reported class.
1 | cfPredict(ensObj, newInputData)
|
ensObj |
The classification ensemble (in the form of an R list) as generated by |
newInputData |
A new independent dataset with unknown classes. The new dataset must have exactly the same number of columns as the |
The cfPredict
function returns an object in the form of an R list. The attributes of the list can be accessed by executing the attributes command. More specifically, the list of attributes includes:
totalPred |
A matrix of the predicted classes as generated by a majority vote between the classifiers in the ensemble along with their confidence scores (the % percentage of the predicted class in the majority vote) for each sample. |
indivPred |
A matrix of the individual classes as predicted by each classifier in the ensemble. Each row represents a sample to be predicted, and each column an independent classification model in the ensemble as generated by |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ## Not run:
data(iris)
irisClass <- iris[,5]
irisData <- iris[,-5]
# Construct a classification ensemble with 100 classifiers and 100 bootstrap
# iterations during optimisation
ens <- cfBuild(irisData, irisClass, bootNum = 100, ensNum = 100, parallel = TRUE,
cpus = 4, type = "SOCK")
# Randomly generate test data to find out their classes using the generated ensemble
# 400 points are selected at random, which results in 100 samples (rows).
# Predict the classes of the data using the classifiers in the constructed ensemble
testMatr <- matrix(runif(400)*100, ncol=4)
predRes <- cfPredict(ens, testMatr)
# Get the attributes of the object predRes
attributes(predRes)$names
# Get the predicted classes as generated by a majority vote between the classifiers
predRes$totalPred
# Get the individual classes as predicted by each classifier in the ensemble
predRes$indivPred
## End(Not run)
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