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SurvSet: An open-source time-to-event dataset repository

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arxiv 2203.03094 v1 pith:AW3AI557 submitted 2022-03-07 stat.ML cs.LG

SurvSet: An open-source time-to-event dataset repository

Erik Drysdale This is my paper
classification stat.ML cs.LG
keywords survsetdatarepositoryalgorithmsdatasetdatasetslikemethods
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Time-to-event (T2E) analysis is a branch of statistics that models the duration of time it takes for an event to occur. Such events can include outcomes like death, unemployment, or product failure. Most modern machine learning (ML) algorithms, like decision trees and kernel methods, are supported for T2E modelling with data science software (python and R). To complement these developments, SurvSet is the first open-source T2E dataset repository designed for a rapid benchmarking of ML algorithms and statistical methods. The data in SurvSet have been consistently formatted so that a single preprocessing method will work for all datasets. SurvSet currently has 76 datasets which vary in dimensionality, time dependency, and background (the majority of which come from biomedicine). SurvSet is available on PyPI and can be installed with pip install SurvSet. R users can download the data directly from the corresponding git repository.

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