scikit-survival 0.3 released

Today, I released a new version of scikit-survival, a Python module for survival analysis built on top of scikit-learn.

This release adds predict_survival_function and predict_cumulative_hazard_function to sksurv.linear_model.CoxPHSurvivalAnalysis, which return the survival function and cumulative hazard function using Breslow’s estimator.

Moreover, it fixes a build error on Windows (#3) and adds the sksurv.preprocessing.OneHotEncoder class, which can be used in a scikit-learn pipeline.

Download

You can install the latest version via pip:

 pip install -U scikit-survival 

or download the source from GitHub.

Unfortunately, I was not able to convince the recently released conda-build 3 to create Anaconda packages, therefore you would need to install from source, for the time being.

Introduction to Survival Analysis with scikit-survival

Finally, I created a notebook that introduces survival analysis (based on my previous post) and shows you how to use the Kaplan-Meier estimator and Cox’s proportional hazards model.

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Sebastian Pölsterl
Post-Doctoral Researcher

My research interests include machine learning for time-to-event analysis, non-Euclidean data, and biomedical applications.