mlr (version 2.3)

plotViperCharts: Visualize binary classification predictions via ViperCharts system.

Description

This includes ROC, lift charts, cost curves, and so on. Please got to http://viper.ijs.si for further info.

For resampled learners, the predictions from different iterations are combined into one. That is, for example for cross-validation, the predictions appear on a single line even though they were made by different models. There is currently no facility to separate the predictions for different resampling iterations.

Usage

plotViperCharts(obj, chart = "rocc", browse = TRUE, auth.key = NULL,
  task.id = NULL)

Value

[character(1)]. Invisibly returns the ViperCharts URL.

References

Sluban and Lavrač - ViperCharts: Visual Performance Evaluation Platform, ECML PKDD 2013, pp. 650-653, LNCS 8190, Springer, 2013.

See Also

Other predict: asROCRPrediction; getProbabilities; plotROCRCurves; predict.WrappedModel; setPredictThreshold; setPredictType

Other roc: asROCRPrediction; plotROCRCurves

Examples

lrn1 = makeLearner("classif.logreg", predict.type = "prob")
lrn2 = makeLearner("classif.rpart", predict.type = "prob")
b = benchmark(list(lrn1, lrn2), pid.task)
z = plotViperCharts(b, chart = "lift", browse = TRUE)