# knn

0th

Percentile

##### k-Nearest Neighbour Classification

k-nearest neighbour classification for test set from training set. For each row of the test set, the k nearest (in Euclidean distance) training set vectors are found, and the classification is decided by majority vote, with ties broken at random. If there are ties for the kth nearest vector, all candidates are included in the vote.

Keywords
classif
##### Usage
knn(train, test, cl, k = 1, l = 0, prob = FALSE, use.all = TRUE)
##### Arguments
train
matrix or data frame of training set cases.
test
matrix or data frame of test set cases. A vector will be interpreted as a row vector for a single case.
cl
factor of true classifications of training set
k
number of neighbours considered.
l
minimum vote for definite decision, otherwise doubt. (More precisely, less than k-l dissenting votes are allowed, even if k is increased by ties.)
prob
If this is true, the proportion of the votes for the winning class are returned as attribute prob.
use.all
controls handling of ties. If true, all distances equal to the kth largest are included. If false, a random selection of distances equal to the kth is chosen to use exactly k neighbours.
##### Value

• factor of classifications of test set. doubt will be returned as NA.

##### References

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

knn1, knn.cv

• knn
##### Examples
train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
knn(train, test, cl, k = 3, prob=TRUE)
attributes(.Last.value)
Documentation reproduced from package class, version 7.3-0, License: GPL-2 | GPL-3

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