scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn

By Sebastian Pölsterl. Journal of machine learning research, 2020

472 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7fd672653caaf3876b7fea945c65b250eeaad912

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. J. Kenneth Tay
2. Balasubramanian Narasimhan
3. Trevor J. Hastie
4. Robert Tibshirani
5. Jianqing Fan
6. Stefan Wager
7. Nicolai Meinshausen
8. Francis R. Bach

Citations in the corpus

• Elastic Net Regularization Paths for All Generalized Linear Models (2021, methodology): “Building on top of scikit-learn, the scikit-survival package (Pölsterl 2020) can fit regularized Cox models for right-censored data, while the relaxed_lasso package (Vial and Estermann 2020) can fit the relaxed lasso for the linear regression model.”