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

Description Usage Arguments Value References See Also Examples

A hyperSMURF model is tested on a given data set. Predictions of each RF of the hyperensemble are performed sequentially and the scores of each ensemble are finally averaged.

1	hyperSMURF.test(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`

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

M. Schubach, M. Re, P.N. Robinson and G. Valentini Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants, Scientific Reports, Nature Publishing, 7:2959, 2017.

hyperSMURF.train

train <- imbalanced.data.generator(n.pos=20, n.neg=1000, 
       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);
test <- imbalanced.data.generator(n.pos=20, n.neg=1000, 
        n.features=10, n.inf.features=2, sd=0.1, seed=2);
res <- hyperSMURF.test(test$data, HSmodel);
y <- ifelse(test$labels==1,1,0);
pred <- ifelse(res>0.5,1,0);
table(pred,y);