kNN.plot | R Documentation |
Visualizing the Optimal Number of k for k-Nearest Neighbour ClassificationkNN
based on accuracy or Mean Square Error (MSE).
kNN.plot( formula, train, test, k.max = 10, transform = FALSE, base = "error",
report = FALSE, set.seed = NULL, ... )
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.max |
the maximum number of number of neighbours to consider, must be at least two. |
transform |
a character with options |
base |
base measurement: |
report |
a character with options |
set.seed |
a single value, interpreted as an integer, or NULL. |
... |
options to be passed to |
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[ 1:150, ]
test = risk[ 151:246, ]
kNN.plot( risk ~ income + age, train = train, test = test )
kNN.plot( risk ~ income + age, train = train, test = test, base = "accuracy" )
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