kNN | R Documentation |
kNN
is used to perform k-nearest neighbour classification for test set using training set. For each row of the test set, the k
nearest (based on Euclidean distance) training set vectors are found. Then, the classification is done by majority vote (ties broken at random). This function provides a formula interface to the knn
function of R
package class
. In addition, it allows normalization of the given data using the transform
function.
kNN( formula, train, test, k = 1, transform = FALSE, type = "class", l = 0,
use.all = TRUE, na.rm = FALSE )
formula |
a formula, with a response but no interaction terms. For the case of data frame, it is taken as the model frame (see |
train |
data frame or matrix of train set cases. |
test |
data frame or matrix of test set cases. |
k |
number of neighbours considered. |
transform |
a character with options |
type |
either |
l |
minimum vote for definite decision, otherwise |
use.all |
controls handling of ties. If true, all distances equal to the |
na.rm |
a logical value indicating whether NA values in |
When type = "class"
(default), a factor vector is returned,
in which the doubt
will be returned as NA
.
When type = "prob"
, a matrix of confidence values is returned
(one column per class).
Reza Mohammadi a.mohammadi@uva.nl and Kevin Burke kevin.burke@ul.ie
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.
knn
, transform
data( risk )
train = risk[ 1:100, ]
test = risk[ 101, ]
kNN( risk ~ income + age, train = train, test = test )
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