generateHyperParsEffectData: Generate hyperparameter effect data.
In shuodata/mlr-master: Machine Learning in R

Description Usage Arguments Value Examples

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.

1 2	generateHyperParsEffectData(tune.result, include.diagnostics = FALSE, trafo = FALSE, partial.dep = FALSE)

`tune.result`	[`TuneResult` \| `ResampleResult`] Result of `tuneParams` (or `resample` ONLY when used for nested cross-validation). The tuning result (or results if the output is from nested cross-validation), also containing the optimizer results. If nested CV output is passed, each element in the list will be considered a separate run, and the data from each run will be included in the dataframe within the returned `HyperParsEffectData`.
`include.diagnostics`	[`logical(1)`] Should diagnostic info (eol and error msg) be included? Default is `FALSE`.
`trafo`	[`logical(1)`] Should the units of the hyperparameter path be converted to the transformed scale? This is only useful when trafo was used to create the path. Default is `FALSE`.
`partial.dep`	[`logical(1)`] Should partial dependence be requested based on converting to reg task? This sets a flag so that we know to use partial dependence downstream. This should most likely be set to `TRUE` if 2 or more hyperparameters were tuned simultaneously. Partial dependence should always be requested when more than 2 hyperparameters were tuned simultaneously. Setting to `TRUE` will cause `plotHyperParsEffect` to automatically plot partial dependence when called downstream. Default is `FALSE`.

[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)