scikit-survival and pycox
These are competitors offering overlapping survival analysis functionality through different ML backends—scikit-survival uses scikit-learn's traditional algorithms while pycox specializes in neural network-based methods via PyTorch, so practitioners typically choose one based on whether they prefer classical or deep learning approaches.
About scikit-survival
sebp/scikit-survival
Survival analysis built on top of scikit-learn
Implements specialized models that account for censored data—where event outcomes are partially unknown—enabling time-to-event predictions in clinical and reliability domains. Integrates seamlessly with scikit-learn's preprocessing, cross-validation, and pipeline infrastructure while supporting both uncensored and right-censored observations. Provides multiple survival model variants optimized through convex solvers (ECOS, OSQP) with dependencies on NumPy, SciPy, and pandas for numerical computation.
About pycox
havakv/pycox
Survival analysis with PyTorch
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