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pRoloc (version 1.12.4)

knnOptimisation: knn parameter optimisation

Description

Classification parameter optimisation for the k-nearest neighbours algorithm.

Usage

knnOptimisation(object, fcol = "markers", k = seq(3, 15, 2), times = 100, test.size = 0.2, xval = 5, fun = mean, seed, verbose = TRUE, ...)

Arguments

object
An instance of class "MSnSet".
fcol
The feature meta-data containing marker definitions. Default is markers.
k
The hyper-parameter. Default values are seq(3, 15, 2).
times
The number of times internal cross-validation is performed. Default is 100.
test.size
The size of test data. Default is 0.2 (20 percent).
xval
The n-cross validation. Default is 5.
fun
The function used to summarise the xval macro F1 matrices.
seed
The optional random number generator seed.
verbose
A logical defining whether a progress bar is displayed.
...
Additional parameters passed to knn from package class.

Value

An instance of class "GenRegRes".

Details

Note that when performance scores precision, recall and (macro) F1 are calculated, any NA values are replaced by 0. This decision is motivated by the fact that any class that would have either a NA precision or recall would result in an NA F1 score and, eventually, a NA macro F1 (i.e. mean(F1)). Replacing NAs by 0s leads to F1 values of 0 and a reduced yet defined final macro F1 score.

See Also

knnClassification and example therein.