K-fold Cross-Validation
K-fold cross-validation.
CVHTF(X, y, K = 10, REP = 1, family = gaussian, ...)
X |
training inputs |
y |
training output |
K |
size of validation sample |
REP |
number of replications |
family |
glm family |
... |
optional arguments passed to |
HTF (2009) describe K-fold cross-validation.
The observations are partitioned into K non-overlapping subsets of approximately
equal size. Each subset is used as the validation sample while the remaining
K-1 subsets are used as training data. When K=n,
where n is the number of observations
the algorithm is equivalent to leave-one-out CV.
Normally K=10 or K=5 are used.
When K<n-1, their are may be many possible partitions and so the results
of K-fold CV may vary somewhat depending on the partitions used.
In our implementation, random partitions are used and we allow for many
replications. Note that in the Shao's delete-d method, random samples are
used to select the valiation data whereas in this method the whole partition
is selected as random. This is acomplished using,
fold <- sample(rep(1:K,length=n)).
Then fold indicates each validation sample in the partition.
Vector of two components comprising the cross-validation MSE and its sd based on the MSE in each validation sample.
A.I. McLeod and C. Xu
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning. 2nd Ed. Springer-Verlag.
#Example 1. 10-fold CV data(zprostate) train<-(zprostate[zprostate[,10],])[,-10] X<-train[,1:2] y<-train[,9] CVHTF(X,y,K=10,REP=1)[1]
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