knn.cv {class} | R Documentation |
k-nearest neighbour cross-validatory classification from training set.
knn.cv(train, cl, k = 1, l = 0, prob = FALSE, use.all = TRUE)
train |
matrix or data frame of training set cases. |
cl |
factor of true classifications of training set |
k |
number of neighbours considered. |
l |
minimum vote for definite decision, otherwise |
prob |
If this is true, the proportion of the votes for the winning class
are returned as attribute |
use.all |
controls handling of ties. If true, all distances equal to the |
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.
Factor of classifications of training set. doubt
will be returned as NA
.
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.
train <- rbind(iris3[,,1], iris3[,,2], iris3[,,3]) cl <- factor(c(rep("s",50), rep("c",50), rep("v",50))) knn.cv(train, cl, k = 3, prob = TRUE) attributes(.Last.value)