View source: R/generateHyperParsEffect.R
generateHyperParsEffectData | R Documentation |
Generate cleaned hyperparameter effect data from a tuning result or from a nested cross-validation tuning result. The object returned can be used for custom visualization or passed downstream to an out of the box mlr method, plotHyperParsEffect.
generateHyperParsEffectData( tune.result, include.diagnostics = FALSE, trafo = FALSE, partial.dep = FALSE )
tune.result |
(TuneResult | ResampleResult) |
include.diagnostics |
( |
trafo |
( |
partial.dep |
( |
(HyperParsEffectData
)
Object containing the hyperparameter effects dataframe, the tuning
performance measures used, the hyperparameters used, a flag for including
diagnostic info, a flag for whether nested cv was used, a flag for whether
partial dependence should be generated, and the optimization algorithm used.
## Not run: # 3-fold cross validation ps = makeParamSet(makeDiscreteParam("C", values = 2^(-4:4))) ctrl = makeTuneControlGrid() rdesc = makeResampleDesc("CV", iters = 3L) res = tuneParams("classif.ksvm", task = pid.task, resampling = rdesc, par.set = ps, control = ctrl) data = generateHyperParsEffectData(res) plt = plotHyperParsEffect(data, x = "C", y = "mmce.test.mean") plt + ylab("Misclassification Error") # nested cross validation ps = makeParamSet(makeDiscreteParam("C", values = 2^(-4:4))) ctrl = makeTuneControlGrid() rdesc = makeResampleDesc("CV", iters = 3L) lrn = makeTuneWrapper("classif.ksvm", control = ctrl, resampling = rdesc, par.set = ps) res = resample(lrn, task = pid.task, resampling = cv2, extract = getTuneResult) data = generateHyperParsEffectData(res) plotHyperParsEffect(data, x = "C", y = "mmce.test.mean", plot.type = "line") ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.