knn.cv {FNN} | R Documentation |
k-nearest neighbour classification cross-validation from training set.
knn.cv(train, cl, k = 1, prob = FALSE, algorithm=c("kd_tree",
"cover_tree", "brute"))
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. |
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
.
distances and indice of k nearest neighbors are also returned as attributes.
Shengqiao Li. To report any bugs or suggestions please email: lishengqiao@yahoo.com
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.
data(iris3)
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)