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scikit-survival
Maintainer K-Dense Inc. · Last updated April 1, 2026
scikit-survival is a Python library for survival analysis built on top of scikit-learn. It provides speciali.
Original source
K-Dense-AI/claude-scientific-skills
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/scikit-survival
- Maintainer
- K-Dense Inc.
- License
- GPL-3.0 license
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- scikit-survival is a Python library for survival analysis built on top of scikit-learn. It provides specialized tools for time-to-event analysis, handling the unique challenge of censored data where some observations are only partially known.
- Survival analysis aims to establish connections between covariates and the time of an event, accounting for censored records (particularly right-censored data from studies where participants don't experience events during observation periods).
- y = Surv.from_arrays(event=event_array, time=time_array).
Source Doc
Excerpt From SKILL.md
When to Use This Skill
Use this skill when:
- Performing survival analysis or time-to-event modeling
- Working with censored data (right-censored, left-censored, or interval-censored)
- Fitting Cox proportional hazards models (standard or penalized)
- Building ensemble survival models (Random Survival Forests, Gradient Boosting)
- Training Survival Support Vector Machines
- Evaluating survival model performance (concordance index, Brier score, time-dependent AUC)
- Estimating Kaplan-Meier or Nelson-Aalen curves
- Analyzing competing risks
- Preprocessing survival data or handling missing values in survival datasets
- Conducting any analysis using the scikit-survival library
1. Model Types and Selection
scikit-survival provides multiple model families, each suited for different scenarios:
Cox Proportional Hazards Models
Use for: Standard survival analysis with interpretable coefficients
CoxPHSurvivalAnalysis: Basic Cox modelCoxnetSurvivalAnalysis: Penalized Cox with elastic net for high-dimensional dataIPCRidge: Ridge regression for accelerated failure time models
See: references/cox-models.md for detailed guidance on Cox models, regularization, and interpretation
Use cases
- Performing survival analysis or time-to-event modeling.
- Working with censored data (right-censored, left-censored, or interval-censored).
- Fitting Cox proportional ha.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
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