knnDS: K-Nearest Neighbour Classification

In isglobal-brge/dsML: DataSHIELD server site machine learning functions.

K-Nearest Neighbour Classification

Description

Compute K-Nearest Neighbours of a query vector

Usage

knnDS(x, neigh, classificator_name, method.indicator, k, noise, ...)

Arguments

x	data frame Dataset to get the neighbours and tags
neigh	numeric number of neighbours considered
classificator_name	character Name of column on the table 'x' that has the classifier factor
method.indicator	character (default "knn") specifies the method that is used to generated non-disclosive coordinates to calculate the euclidean distance. This argument can be set as 'knn' or 'noise'
k	numeric (default 3) he number of the nearest neighbors for which their centroid is calculated
noise	numeric (default 0.25) the percentage of the initial variance that is used as the variance of the embedded noise if the argument method is set to 'noise'
...	numeric Queried vector

Value

list with:
-distance numeric: Distances of the queried vector to the anonimized dataset
-classification character: Clasification tag of the queried vector

isglobal-brge/dsML documentation built on March 14, 2023, 1:58 p.m.