gcv(X, y, MaxIter = 1000, d = ceiling(length(y)/10), NCores = 1, cost = mse, yhat = yhat_lm, libs = character(0), seed = "default", ...)
set.seed
to control the random. But I have included it as an argument
since it can be used to set the parallel random number generator seed. This
is sometimes useful for replicating the simulations. If the argument
seed
is used, it will also set the seed when only serial computation
is done.mse
,
mae
,
mape
,
misclassificationrate
,
logloss
,
yhat_lm
,
yhat_nn
,
yhat_lars
,
yhat_plus
,
yhat_gel
,
yhat_step
,
yh_lda
,
yh_qda
,
yh_svm
,
yh_NB
,
yh_RF
,
yh_CART
,
yh_C50
,
yh_kNN
,
featureSelect
,
cv.glm
#Simple example but in general, MaxIter >= 1000 is recommended.
Xy <- ShaoReg()
gcv(Xy[,1:8], Xy[,9], MaxIter=25, d=5)
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