# knn

From class v7.3-0
by Brian Ripley

##### 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
`k`

th 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
`k`

th largest are included. If false, a random selection of distances equal to the`k`

th 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.

##### See Also

##### 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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