scikit-survival is a Python module for survival analysis
built on top of scikit-learn. It allows doing survival analysis
while utilizing the power of scikit-learn, e.g., for pre-processing or doing cross-validation.
The objective in survival analysis (also referred to as time-to-event or reliability analysis)
is to establish a connection between covariates and the time of an event.
What makes survival analysis differ from traditional machine learning is the fact that
parts of the training data can only be partially observed – they are censored.
For instance, in a clinical study, patients are often monitored for a particular time period,
and events occurring in this particular period are recorded.
If a patient experiences an event, the exact time of the event can
be recorded – the patient’s record is uncensored. In contrast, right censored records
refer to patients that remained event-free during the study period and
it is unknown whether an event has or has not occurred after the study ended.
Consequently, survival analysis demands for models that take
this unique characteristic of such a dataset into account.
The easiest way to install scikit-survival is to use
Anaconda by running:
conda install -c sebp scikit-survival
Alternatively, you can install scikit-survival from source
following this guide.
The user guide provides in-depth information on the key concepts of scikit-survival, an overview of available survival models, and hands-on examples.
The reference guide contains a detailed description of the scikit-survival API. It describes which classes and functions are available and what their parameters are.
Saw a typo in the documentation? Want to add new functionalities? The contributing guidelines will guide you through the process of setting up a development environment and submitting your changes to the scikit-survival team.