# scikit-survival 0.3 released

• 1 August 2017
• sebp

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.

pip install -U scikit-survival

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.

### GradientBoostingSurvivalAnalysis use case + predict_survival_fnc

Hi Sebastian,

First of all, thank you for making this! I'm using the GradientBoostingSurvivalAnalysis algorithm and it's getting great concordant results, but I'm at a loss as to how to interpret the float predictions it outputs. I'm very new to survival analysis and am coming to this from a more general classification machine learning background. I'm trying to use this to calculate the probability of an event 6 time periods in the future. I don't know how to do this with the GradientBoostingSurvivalAnalysis despite reading your docs. For now, I'll use the predict_survival_function from the Cox algorithm to get the hazard probability.

Thanks!
Nate

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