HΛZΛRDOUS

Gradient-boosting survival analysis

• survival and competing risks

• scikit-learn compatible

• scalable gradient boosting

A scalable time-to-event and competing risk prediction model implemented in Python.

Competing risks settings

Predicting which event will happen first, and when, from data where some events have not yet been observed:

The model is a gradient-boosting variant, that offers prediction for survival and competing risks settings, fully compatible with scikit-learn. It can be used with scikit-learn tools such as pipelines, column transformers, cross-validation, hyper-parameter search tools, etc.

This library puts a focus on predictive accuracy rather than on inference. Quantifying the statistical association or causal effect of covariates with/on the cumulated event incidence or instantaneous hazard rate is not in the scope of this library at this time.

The theory behind the model is described in this paper.

• License: MIT

• GitHub repository: soda-inria/hazardous

• Changelog: soda-inria/hazardous

• Status: under development, API is subject to change without notice.

Contents:

• API
  • Estimators
  • Metrics
  • Datasets
• Examples
• Getting the SEER dataset