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.
knn(train, test, cl, k = 1, l = 0, prob = FALSE, use.all = TRUE)
- matrix or data frame of training set cases.
- matrix or data frame of test set cases. A vector will be interpreted as a row vector for a single case.
- factor of true classifications of training set
- number of neighbours considered.
- minimum vote for definite decision, otherwise
doubt. (More precisely, less than
k-ldissenting votes are allowed, even if
kis increased by ties.)
- If this is true, the proportion of the votes for the winning class are returned as attribute
- 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
Factor of classifications of test set.
doubt will be returned as
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.
Documentation reproduced from package class, version 7.3-7. License: GPL-2 | GPL-3