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.
Installation
$ pip install random-survival-forest
Contribute
Getting Started
>>> from random_survival_forest import RandomSurvivalForest
>>> timeline = range(0, 10, 1)
>>> rsf = RandomSurvivalForest(n_estimators=20, timeline=timeline)
>>> rsf.fit(X, y)
>>> round(rsf.oob_score, 3)
0.76
>>> y_pred = rsf.predict(X_val)
>>> c_val = concordance_index(y_val["time"], y_pred, y_val["event"])
>>> round(c_val, 3)
0.72
Support
If you are having issues or feedback, please let me know.
julian.spaeth@student.uni-tuebinden.de
License
MIT
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file random_survival_forest-0.7.2.tar.gz.
File metadata
- Download URL: random_survival_forest-0.7.2.tar.gz
- Upload date:
- Size: 6.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.4.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
aa2a7a34effa27d5ecb22ffd638b63ef994586abad20096b8a46139aa9de4607
|
|
| MD5 |
58cfd859664c4e688f2bcc37c17f4971
|
|
| BLAKE2b-256 |
c5d590fbbf9d2cc9ae8cd6d7efdb19a8319c24b0e40a9ddafc6fd40d98cba6e5
|
File details
Details for the file random_survival_forest-0.7.2-py3-none-any.whl.
File metadata
- Download URL: random_survival_forest-0.7.2-py3-none-any.whl
- Upload date:
- Size: 9.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.4.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
4dfd733dae1e1cabbd7b95e9b0e347f213b05f5b50e1ec5b022051243500e3b1
|
|
| MD5 |
d2e69ff85b29d46a9f561a2eaa0c1856
|
|
| BLAKE2b-256 |
c7ece90ccb9e55991b9d8c746ff7371a78364d5ec6f40fe44000c479920b15a3
|
