kNN.plot {liver} | 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, scaler = FALSE,
base = "accuracy", 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 neighbors to consider can either be a single value, with a minimum of 2, or a vector representing a range of values k. |
scaler |
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.
data(risk)
partition_risk <- partition(data = risk, ratio = c(0.6, 0.4))
train = partition_risk$part1
test = partition_risk$part1
kNN.plot(risk ~ income + age, train = train, test = test)
kNN.plot(risk ~ income + age, train = train, test = test, base = "error")