# k-Nearest Neighbour Classification

### Description

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.

### Usage

1 |

### 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. |

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

`algorithm` |
nearest neighbor search algorithm. |

### Value

factor of classifications of test set. `doubt`

will be returned as `NA`

.

### Author(s)

Shengqiao Li. To report any bugs or suggestions please email: shli@stat.wvu.edu.

### References

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.

### See Also

`ownn`

, `knn.cv`

and `knn`

in class.

### Examples

1 2 3 4 5 6 |

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.