  Bias - variance decomposition of the expected error defined for regression
and classification problems is an important tool to study and compare different
algorithms, to find the best areas for their application. Here the
decomposition is introduced for the survival analysis problem. In our
experiments, we study bias -variance parts of the expected error for two
algorithms: original Cox proportional hazard regression and CoxPath, path
algorithm for L1-regularized Cox regression, on the series of increased
training sets. The experiments demonstrate that, contrary expectations, CoxPath
does not necessarily have an advantage over Cox regression.
