2011/09/24 by Marina Sapir, Sapir, Marina
Computer Science · Mathematics · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1109.5311
openalex publication_date 2011/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.