sebp/scikit-survival

Survival analysis built on top of scikit-learn

What it solves

It provides tools for survival analysis (also known as time-to-event or reliability analysis), which is used to establish a connection between covariates and the time an event occurs. Unlike traditional machine learning, it specifically handles "censored" data—where some events have not yet occurred or were not observed during the study period.

How it works

Built on top of scikit-learn, the library integrates survival analysis models into a familiar machine learning pipeline. This allows users to leverage scikit-learn's existing capabilities for data pre-processing and cross-validation while applying specialized models that account for censored observations.

Who it’s for

Data scientists and researchers, particularly in clinical studies or reliability engineering, who need to analyze time-to-event data with censored records.

Highlights

  • Built on top of scikit-learn for seamless integration with ML pipelines.
  • Specifically designed to handle right-censored data.
  • Provides a variety of survival models and tools for pre-processing and cross-validation.

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