# k-Nearest Neighbour Classification Cross-Validation

### Description

k-nearest neighbour classification cross-validation from training set.

### Usage

1 |

### Arguments

`train` |
matrix or data frame of training set cases. |

`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. |

### Details

This uses leave-one-out cross validation.
For each row of the training set `train`

, the `k`

nearest
(in Euclidean distance) other 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.

### Value

factor of classifications of training set. `doubt`

will be returned as `NA`

.
distances and indice of k nearest neighbors are also returned as attributes.

### Author(s)

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

### 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

`knn`

and `knn.cv`

in class.

### Examples

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