A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.
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Nov 11, 2024 - Python
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random-survival-forests
Here are 11 public repositories matching this topic...
geneSurv: an interactive web-based tool for survival analysis in genomics research
survival-analysis hazard-ratios kaplan-meier schoenfeld-residuals cox-regression interactive-plots random-survival-forests cutoff-for-survival
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Mar 17, 2020 - HTML
Building Explanations through a LocaLly AccuraTe Rule EXtractor
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Nov 3, 2025 - Python
Federated Learning with Random Survival Forests.
random-forest artificial-intelligence survival-analysis ensemble-learning random-survival-forests federated-learning
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Jan 26, 2024 - Python
Survival learners for the `mlexperiments` R 📦
machine-learning r algorithms xgboost glmnet survival experiments r-package cox-regression random-survival-forests learners survival-support-vector-machine
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Dec 5, 2025 - R
Predicting customers' churn with survival analysis, including time-dependent variables
python r survival-analysis cox-regression random-survival-forests survival-prediction customer-churn-prediction time-dependent-covariates
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Oct 25, 2022 - R
An interactive Streamlit dashboard for analyzing equipment failure patterns and predicting maintenance needs. Features include data visualization, root cause analysis using Apriori algorithm, and survival modeling with Random Survival Forests to estimate time-to-failure and optimize spare parts management.
machine-learning plotly pandas data-visualization seaborn survival-analysis matplotlib random-survival-forests association-rules apriori-algorithm predictive-maintenance streamlit scikit-survival
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Jun 23, 2025 - Jupyter Notebook
Survival analysis of stomach cancer, using TCGA gene expression data.
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Dec 3, 2025 - R
reproducibility results for IAL2023 workshop
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Sep 21, 2023 - Python
Survival analysis applied to a breast cancer dataset, including information on hormone therapy, chemotherapy, radiotherapy, and time-to-event variables. The study uses Kaplan–Meier curves, GLM models, and a Random Survival Forest to evaluate survival patterns and risk.
biostatistics survival-analysis kaplan-meier random-survival-forests r-programming breast-cancer medical-statistics clinical-survival
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Dec 8, 2025 - HTML
Risk assessment via survival modelling
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May 27, 2024 - HTML
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