Evaluation for k-Nearest-Neighbors (kNN) classification by cross-validation
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X |
standardized complete X data matrix (training and test data) |
grp |
factor with groups for complete data (training and test data) |
train |
row indices of X indicating training data objects |
kfold |
number of folds for cross-validation |
knnvec |
range for k for the evaluation of kNN |
plotit |
if TRUE a plot will be generated |
legend |
if TRUE a legend will be added to the plot |
legpos |
positioning of the legend in the plot |
... |
additional plot arguments |
The data are split into a calibration and a test data set (provided by "train"). Within the calibration set "kfold"-fold CV is performed by applying the classification method to "kfold"-1 parts and evaluation for the last part. The misclassification error is then computed for the training data, for the CV test data (CV error) and for the test data.
trainerr |
training error rate |
testerr |
test error rate |
cvMean |
mean of CV errors |
cvSe |
standard error of CV errors |
cverr |
all errors from CV |
knnvec |
range for k for the evaluation of kNN, taken from input |
Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.
knn
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