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

Bootstrap Bias Corrected Cross Validation applied to Super Learning