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.
1 |
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. |
factor of classifications of test set. doubt
will be returned as NA
.
Shengqiao Li. To report any bugs or suggestions please email: shli@stat.wvu.edu.
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.
ownn
, knn.cv
and knn
in class.
1 2 3 4 5 6 |
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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