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Bias - variance decomposition of the expected error defined for regressionand classification problems is an important tool to study and compare differentalgorithms, to find the best areas for their application. Here thedecomposition is introduced for the survival analysis problem. In ourexperiments, we study bias -variance parts of the expected error for twoalgorithms: original Cox proportional hazard regression and CoxPath, pathalgorithm for L1-regularized Cox regression, on the series of increasedtraining sets. The experiments demonstrate that, contrary expectations, CoxPathdoes not necessarily have an advantage over Cox regression.

Bias Plus Variance Decomposition for Survival Analysis Problems