hyperSMURF.test.thresh: Test of a thresholded hyperSMURF model
In hyperSMURF: Hyper-Ensemble Smote Undersampled Random Forests

Description Usage Arguments Value See Also Examples

The predictions of each random forest are discrete, i.e. 1 or 0: the probabilities are thresholded according to the cutoff value set in the training phase. The threshold is embedded in the HSmodel according to the cutoff parameter set in the training phase. The score computed by the hyperensemble is the average of the discrete predictions generated by each base random forest.

1	hyperSMURF.test.thresh(data, HSmodel)

`data`	a data frame or matrix with the test data. Rows: examples; columns: features
`HSmodel`	a list including the trained random forest models. The models have been trained with `hyperSMURF.train`. The threshold is embedded in the model according to the `cutoff` value set in the training phase.

a named vector with the computed probabilities for each example (HyeprSMURF thresholded score)

hyperSMURF.test, hyperSMURF.train

train <- imbalanced.data.generator(n.pos=20, n.neg=500, 
         n.features=10, n.inf.features=2, sd=0.1, seed=1);
HSmodel <- hyperSMURF.train(train$data, train$label, n.part = 5, 
                  fp = 1, ratio = 2, k = 5, cutoff=c(0.3, 0.7));
test <- imbalanced.data.generator(n.pos=20, n.neg=500, 
         n.features=10, n.inf.features=2, sd=0.1, seed=2);
res <- hyperSMURF.test.thresh(test$data, HSmodel);