measures
(Package: mlr) :
Performance measures.
A performance measure is evaluated after a single train/predict step and returns a single number to assess the quality of the prediction (or maybe only the model, think AIC). The measure itself knows whether it wants to be minimized or maximized and for what tasks it is applicable.
● Data Source:
CranContrib
● Keywords: datasets
● Alias: G1, G2, acc, arsq, auc, bac, ber, brier, cindex, db, dunn, expvar, f1, fdr, featperc, fn, fnr, fp, fpr, gmean, gpr, hamloss, mae, mcc, mcp, meancosts, measureACC, measureAUC, measureBAC, measureBrier, measureEXPVAR, measureFDR, measureFN, measureFNR, measureFP, measureFPR, measureGMEAN, measureGPR, measureHAMLOSS, measureMAE, measureMCC, measureMEDAE, measureMEDSE, measureMMCE, measureMSE, measureNPV, measurePPV, measureRMSE, measureRSQ, measureSAE, measureSSE, measureTN, measureTNR, measureTP, measureTPR, measures, medae, medse, mmce, mse, multiclass.auc, npv, ppv, rmse, rsq, sae, silhouette, sse, timeboth, timepredict, timetrain, tn, tnr, tp, tpr
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aggregations
(Package: mlr) :
Aggregation methods.
- test.mean
Mean of performance values on test sets.
● Data Source:
CranContrib
● Keywords: datasets
● Alias: aggregations, b632, b632plus, test.join, test.max, test.mean, test.median, test.min, test.range, test.rmse, test.sd, test.sum, testgroup.mean, train.max, train.mean, train.median, train.min, train.range, train.rmse, train.sd, train.sum
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providing 
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