/scikit-survival

name: scikit-survival
description: Build, evaluate, and audit right-censored or competing-risk survival workflows with scikit-survival, including leakage-safe preprocessing, model selection, probability prediction, and censoring-aware metrics.
license: MIT
compatibility: Requires Python 3.11+, uv, and the pinned scikit-survival 0.28.0 stack for executable examples. Bundled CLIs are local and network-free by default.
allowed-tools: Read Write Edit Bash
metadata:
  version: "1.2"
  skill-author: K-Dense Inc.

scikit-survival

Scope

Use this skill for scikit-survival 0.28.0 workflows involving:

• right-censored structured outcomes;
• Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs;
• discrimination, prediction error, calibration-oriented checks, and time-dependent prediction;
• nonparametric cumulative incidence with competing risks;
• scikit-learn pipelines, nested model selection, and reproducible reports.

scikit-survival primarily models right-censored outcomes. Its built-in competing-risk support is nonparametric cumulative incidence; it does not provide Fine-Gray regression. Do not present model output as clinical advice, causal evidence, or proof of clinical utility.

Current release and installation

Verified 2026-07-23:

• Latest stable: **scikit-survival 0.28.0**, released 2026-07-05.
• Python: **3.11 or later**; PyPI wheels cover CPython 3.11-3.14 on Linux

x86-64, macOS x86-64/ARM64, and Windows x86-64.

• Runtime bounds: NumPy >=2.0.0, pandas >=2.2.0, SciPy >=1.13.0,

scikit-learn >=1.9.0,<1.10, OSQP >=1.0.2, narwhals >=2.0.1.

• 0.28 adds pandas/Polars estimator support through narwhals and removes

`criterion` from `GradientBoostingSurvivalAnalysis`.

Create an isolated environment and install the tested snapshot:

uv venv --python 3.11
source .venv/bin/activate
uv pip install \
  "scikit-survival==0.28.0" \
  "scikit-learn==1.9.0" \
  "numpy==2.4.6" \
  "pandas==3.0.5" \
  "scipy==1.17.1" \
  "ecos==2.0.14" \
  "osqp==1.1.3" \
  "joblib==1.5.3" \
  "numexpr==2.14.2" \
  "narwhals==2.24.0"

Binary wheels are preferred. A source build requires a C/C++ compiler; OSQP may also require CMake. This skill is MIT-licensed; the upstream scikit-survival package is GPL-3.0-or-later, so review upstream licensing before redistribution.

Non-negotiable workflow

1. **Define the estimand and event coding.** Decide whether the target is all-event survival, cause-specific hazard, or cause-specific cumulative incidence. 2. **Validate outcomes.** Standard estimators need a two-field structured array: boolean event first, observed time second. Competing-risk CIF instead needs a separate integer event vector: 0=censored, 1..K=causes. 3. **Split before learned preprocessing.** Never fit imputers, encoders, scalers, feature selectors, or alpha choices on all rows before splitting. 4. **Fit preprocessing inside a pipeline.** Unknown categories and missingness must be handled using training-fold state only. 5. **Tune without reusing evaluation data.** Use nested CV when reporting cross-validated tuned performance, or reserve a truly untouched final holdout. 6. **Fit censoring distributions on training data.** IPCW concordance, dynamic AUC, and Brier metrics receive `survival_train`, never a pooled train+test outcome. 7. **Restrict evaluation times.** Use a strictly increasing grid inside test follow-up and below the end of training support where the estimated censoring survival remains positive. 8. **Match predictions to metrics.** Concordance/dynamic AUC consume higher-is-riskier scores. Brier metrics consume survival probabilities with shape `(n_test, n_times)`, not risk scores or unevaluated step functions. 9. **Handle competing causes explicitly.** Standard survival probabilities and CIFs answer different questions. Never estimate event-specific probability with `1 - Kaplan-Meier` while censoring competing events. 10. **Report limits.** Separate discrimination, calibration, prediction error, and cumulative incidence. None alone establishes decision or clinical utility.

Outcome construction

from sksurv.util import Surv

y = Surv.from_arrays(event=event_bool, time=observed_time)
# Equivalent for pandas or Polars:
y = Surv.from_dataframe("event", "time", frame)

The first field is boolean (`True`=event, `False`=right-censored); the second is floating-point time. Field names may vary, but field order and meaning may not. Use `references/data-handling.md` before loading custom or competing-risk data.

Leakage-safe pipeline

from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sksurv.linear_model import CoxPHSurvivalAnalysis

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, stratify=y["event"], random_state=20260723
)

preprocess = ColumnTransformer(
    [
        ("num", make_pipeline(SimpleImputer(strategy="median"), StandardScaler()), numeric),
        (
            "cat",
            make_pipeline(
                SimpleImputer(strategy="most_frequent"),
                OneHotEncoder(handle_unknown="ignore", drop="first", sparse_output=False),
            ),
            categorical,
        ),
    ],
    sparse_threshold=0.0,
)
model = make_pipeline(preprocess, CoxPHSurvivalAnalysis(alpha=0.1, ties="efron"))
model.fit(X_train, y_train)
risk = model.predict(X_test)

The split precedes every learned transformation. For repeated or grouped records, use a group-aware split; for temporal deployment, use a time-respecting split.

Model choice

• `CoxPHSurvivalAnalysis`: interpretable log-hazard coefficients under proportional

hazards; `alpha` is ridge shrinkage and `ties` is `"breslow"` or `"efron"`.

• `CoxnetSurvivalAnalysis`: LASSO/elasti