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.

```
knn(train, test, cl, k = 1, prob = FALSE, algorithm=c("kd_tree",
"cover_tree", "brute"))
```

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.

prob

if this is true, the proportion of the votes for the winning class
are returned as attribute `prob`

.

algorithm

nearest neighbor search algorithm.

factor of classifications of test set. `doubt`

will be returned as `NA`

.

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

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

# NOT RUN { data(iris3) 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) # }