The User Guide covers the most important aspects of doing to survival analysis with scikit-survival.
It is assumed that users have a basic understanding of survival analysis. If you are brand-new to survival analysis, consider studying the basics first, e.g. by reading an introductory book, such as
- David G. Kleinbaum and Mitchel Klein (2012), Survival Analysis: A Self-Learning Text, Springer.
- John P. Klein and Melvin L. Moeschberger (2003), Survival Analysis: Techniques for Censored and Truncated Data, Springer.
Users new to scikit-survival should read :ref:`understanding_predictions` to get familiar with the basic concepts. The interactive guide :ref:`/user_guide/00-introduction.ipynb` gives a brief overview of how to use scikit-survival for survival analysis. Once you are familiar with the basics, it is highly recommended reading the guide :ref:`/user_guide/evaluating-survival-models.ipynb`, which discusses common pitfalls when evaluating the predictive performance of survival models. Finally, there are several model-specific guides that discuss details about particular models, with many examples throughout.
.. toctree:: :maxdepth: 1 understanding_predictions 00-introduction evaluating-survival-models
.. toctree:: :maxdepth: 1 coxnet random-survival-forest boosting survival-svm