plotViperCharts: Visualize binary classification predictions via ViperCharts...
In riebetob/mlr: Machine Learning in R

Description Usage Arguments Value References See Also Examples

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.

1 2	plotViperCharts(obj, chart = "rocc", browse = TRUE, auth.key = NULL, task.id = NULL)

`obj`	[(list of) `Prediction` \| (list of) `ResampleResult` \| `BenchmarkResult`] Single prediction object, list of them, single resample result, list of them, or a benchmark result. In case of a list probably produced by different learners you want to compare, then name the list with the names you want to see in the plots, probably learner shortnames or ids.
`chart`	[`character(1)`] First chart to display in focus in browser. All other charts can be displayed by clicking on the browser page menu. Default is “rocc”.
`browse`	[`logical(1)`] Open ViperCharts plot in web browser? If not you simple get the URL returned. Calls `browseURL`. Default is `TRUE`.
`auth.key`	[`character(1)`] API key to use for call to Viper charts website. Only required if you want the chart to be private. Default is `NULL`.
`task.id`	[`character(1)`] Selected task in `BenchmarkResult` to do plots for, ignored otherwise. Default is first task.

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

Sluban and Lavra<c4><8d> - ViperCharts: Visual Performance Evaluation Platform, ECML PKDD 2013, pp. 650-653, LNCS 8190, Springer, 2013.

Other roc: asROCRPrediction, calculateROCMeasures

Other predict: asROCRPrediction, getPredictionProbabilities, getPredictionResponse, getPredictionTaskDesc, predict.WrappedModel, setPredictThreshold, setPredictType

## Not run: 
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)

## End(Not run)