fastCV {CVST} | R Documentation |
CVST is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation.
fastCV(train, learner, params, setup, test = NULL, verbose = TRUE)
train |
The data set as |
learner |
The learner as |
params |
the parameter grid as |
setup |
A |
test |
An independent test set that should be used at each step. If
|
verbose |
Should the procedure report the performance after each step? |
Returns the optimal parameter settings as determined by fast cross-validation via sequential testing.
Tammo Krueger <tammokrueger@googlemail.com>
Tammo Krueger, Danny Panknin, and Mikio Braun. Fast cross-validation via sequential analysis. Neural Information Processing Systems (NIPS), Big Learning Workshop, 2011. URL http://biglearn.org/2011/index.php/Papers\#paper2.
Tammo Krueger, Danny Panknin, and Mikio Braun. Fast cross-validation via sequential testing. CoRR, abs/1206.2248, 2012. URL http://arxiv.org/abs/1206.2248.
CV
constructCVSTModel
constructData
constructLearner
constructParams
ns = noisySine(100) svm = constructSVMLearner() params = constructParams(kernel="rbfdot", sigma=10^(-3:3), nu=c(0.05, 0.1, 0.2, 0.3)) opt = fastCV(ns, svm, params, constructCVSTModel())