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Found 5 papers, 2 papers with code
Evaluating machine learning models in non-standard settings: An overview and new findings
Our findings corroborate the concern that standard resampling methods often yield biased GE estimates in non-standard settings, underscoring the importance of tailored GE estimation.
Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study
Frequently, however, in the training data and the data to which automatic prediction rules should be applied, the test data, the different omics data types are not available for all patients.
Sequential Permutation Testing of Random Forest Variable Importance Measures
This problem also occurs with non-parametric permutation tests, which are, however, distribution-free and can generically be applied to any type of RF and VIMP.
Large-scale benchmark study of survival prediction methods using multi-omics data
The Kaplan-Meier estimate and a Cox model using only clinical variables were used as reference methods.
A U-statistic estimator for the variance of resampling-based error estimators
Thus, it has minimal variance among all unbiased estimators and is asymptotically normally distributed.
