scikit-survival 0.5 released

Today, I released a new version of scikit-survival. This release adds support for the latest version of scikit-learn (0.19) and pandas (0.21). In turn, support for Python 3.4, scikit-learn 0.18 and pandas 0.18 has been dropped.

Many people are confused about the meaning of predictions. Often, they assume that predictions of a survival model should always be non-negative since the input is the time to an event. However, this not always the case. In general, predictions are risk scores of arbitrary scale. In particular, survival models usually do not predict the exact time of an event, but the relative order of events. If samples are ordered according to their predicted risk score (in ascending order), one obtains the sequence of events, as predicted by the model. A more detailed explanation is available in the Understanding Predictions in Survival Analysis section of the documentation.

Download

You can install the latest version via Anaconda (Linux, OSX and Windows):

conda install -c sebp scikit-survival

or via pip:

pip install -U scikit-survival

Comments

Loving the package. Is there a way to pull survival functions (a la Cox PH models) from your SVM and GBM estimators?

Hi thanks for sharing such a nice package. Can random survival forest be implemented with this package?
Can you share some examples if not too troublesome? Thanks in advance.