Metadata-Version: 2.1
Name: random-survival-forest
Version: 0.4
Summary: A Random Survival Forest implementation inspired by Ishwaran et al.
Home-page: https://github.com/julianspaeth/random-survival-forest
Author: Julian Späth
Author-email: spaethju@posteo.de
License: MIT License
Download-URL: https://github.com/julianspaeth/random-survival-forest/archive/v0.1-alpha.tar.gz
Description: # Random Survival Forest
        
        [![Build Status](https://api.travis-ci.com/julianspaeth/random-survival-forest.svg?branch=master)](https://travis-ci.org/julianspaeth/random-survival-forest) [![Documentation Status](https://readthedocs.org/projects/random-survival-forest/badge/?version=latest)](https://random-survival-forest.readthedocs.io/en/latest/?badge=latest)
        
        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
        ```sh
        $ pip install random-survival-forest
        ```
        
        ## Contribute
        
        - Source Code: https://github.com/julianspaeth/random-survival-forest
        
        ## Getting Started
        
        ```python
        >>> 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
        
Keywords: survival-analysis,survival-prediction,machine-learning,random-forest,random-survival-forest
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
