  Super learner algorithm can be applied to combine results of multiple base
learners to improve quality of predictions. The default method for verification
of super learner results is by nested cross validation. It has been proposed by
Tsamardinos et al., that nested cross validation can be replaced by resampling
for tuning hyper-parameters of the learning algorithms. We apply this idea to
verification of super learner and compare with other verification methods,
including nested cross validation. Tests were performed on artificial data sets
of diverse size and on seven real, biomedical data sets. The resampling method,
called Bootstrap Bias Correction, proved to be a reasonably precise and very
cost-efficient alternative for nested cross validation.
