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 |
a named vector with the computed probabilities for each example (HyeprSMURF thresholded score)
hyperSMURF.test
, hyperSMURF.train
1 2 3 4 5 6 7 | 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);
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