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A Random Survival Forest implementation inspired by Ishwaran et al.

Project description

Random Survival Forest


The Random Survival Forest package provides a python implementation of the survival prediction method originally published by Ishwaran et al. (2008).

Reference: Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. The annals of applied statistics, 2(3), 841-860.


$ pip install random-survival-forest


Getting Started

from random_survival_forest.models import RandomSurvivalForest
from random_survival_forest.scoring import concordance_index
from lifelines import datasets
from sklearn.model_selection import train_test_split

rossi = datasets.load_rossi()
# Attention: duration column must be index 0, event column index 1 in y
y = rossi.loc[:, ["week", "arrest"]]
X = rossi.drop(["arrest", "week"], axis=1)
X, X_test, y, y_test = train_test_split(X, y, test_size=0.25)

rsf = RandomSurvivalForest(n_estimators=20, n_jobs=-1)
rsf =, y)
y_pred = rsf.predict(X_test)
c_val = concordance_index(y_time=y_test["week"], y_pred=y_pred, y_event=y_test["arrest"])
print("C-index", round(c_val, 3))


If you are having issues or feedback, please let me know. I am happy to fix some bug or implement feature requests.

This package is completely open-source. If it helped you or you even use it comercially, I would be happy about a little support:

"Buy Me A Coffee"



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