View source: R/relativeOverfitting.R
estimateRelativeOverfitting | R Documentation |
Estimates the relative overfitting of a model as the ratio of the difference in test and train performance to the difference of test performance in the no-information case and train performance. In the no-information case the features carry no information with respect to the prediction. This is simulated by permuting features and predictions.
estimateRelativeOverfitting(
predish,
measures,
task,
learner = NULL,
pred.train = NULL,
iter = 1
)
predish |
(ResampleDesc | ResamplePrediction | Prediction) |
measures |
(Measure | list of Measure) |
task |
(Task) |
learner |
(Learner | |
pred.train |
(Prediction) |
iter |
(integer) |
Currently only support for classification and regression tasks is implemented.
(data.frame). Relative overfitting estimate(s), named by measure(s), for each resampling iteration.
Bradley Efron and Robert Tibshirani; Improvements on Cross-Validation: The .632+ Bootstrap Method, Journal of the American Statistical Association, Vol. 92, No. 438. (Jun., 1997), pp. 548-560.
Other performance:
ConfusionMatrix
,
calculateConfusionMatrix()
,
calculateROCMeasures()
,
makeCostMeasure()
,
makeCustomResampledMeasure()
,
makeMeasure()
,
measures
,
performance()
,
setAggregation()
,
setMeasurePars()
task = makeClassifTask(data = iris, target = "Species")
rdesc = makeResampleDesc("CV", iters = 2)
estimateRelativeOverfitting(rdesc, acc, task, makeLearner("classif.knn"))
estimateRelativeOverfitting(rdesc, acc, task, makeLearner("classif.lda"))
rpred = resample("classif.knn", task, rdesc)$pred
estimateRelativeOverfitting(rpred, acc, task)
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