TeaPearce/Censored_Quantile_Regression_NN
NeurIPS paper 'Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis'
This project helps researchers and data scientists predict when an event will occur, even if some subjects haven't experienced the event yet (censored data). It takes datasets with survival information and produces a model for analyzing event likelihood over time. It's designed for quantitative analysts, statisticians, or machine learning practitioners working with time-to-event data.
No commits in the last 6 months.
Use this if you need to perform survival analysis and predict event times using neural networks, especially when dealing with censored data without making strong assumptions about the underlying data distribution.
Not ideal if you require traditional statistical methods for survival analysis or prefer simpler, interpretable linear models over neural network approaches.
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2
Language
Jupyter Notebook
License
MIT
Last pushed
Oct 28, 2022
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