2020
JMLR
JMLR 2020
scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn
Abstract
scikit-survival is an open-source Python package for time-to-event analysis fully compatible with scikit-learn. It provides implementations of many popular machine learning techniques for time-to-event analysis, including penalized Cox model, Random Survival Forest, and Survival Support Vector Machine. In addition, the library includes tools to evaluate model performance on censored time-to-event data. The documentation contains installation instructions, interactive notebooks, and a full description of the API. scikit-survival is distributed under the GPL-3 license with the source code and detailed instructions available at https://github.com/sebp/scikit-survival [abs] [ pdf ][ bib ] [ code ] © JMLR 2020. (edit, beta)
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Interdisciplinary Bridge
— Data Science & Analytics and Healthcare & Medicine and Machine Learning
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Keyword Pioneer
— random survival forest
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio