数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-machine-learning-model-validation

维护者 FreedomIntelligence · 最近更新 2026年4月1日

Cross-validation, AUC-ROC, calibration, and permutation testing.

OpenClawNanoClaw分析处理复现实验bio-machine-learning-model-validation🧠 bioos extended suitebioos extended bioinformatics suitecross

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-machine-learning-model-validation

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

outer_cv = StratifiedKFold(n_splits=5，shuffle=True，random_state=42) inner_cv = StratifiedKFold(n_splits=3，shuffle=True，random_state=42)。
nested_scores = [] ，用于 train_idx，test_idx in outer_cv.split(X，y)：X_train，X_test = X.iloc[train_idx]，X.iloc[test_idx] y_train，y_test = y[train_idx]，y[test_idx]。
grid = GridSearchCV(pipe，param_grid，cv=inner_cv，scoring='roc_auc'，n_jobs=-1) grid.fit(X_train，y_train) score = grid.score(X_test，y_test) nested_scores.append(score)。
print(f'Nested CV AUC：{np.mean(nested_scores)：.3f} +/- {np.std(nested_scores)：.3f}')。
cv = StratifiedKFold(n_splits=5，shuffle=True，random_state=42) scores = cross_val_score(pipe，X，y，cv=cv，scoring='roc_auc') print(f'CV AUC：{scores.mean()：.3f} +/- {scores.std()：.3f}')。

原始文档

SKILL.md 摘录

Why Nested CV Matters

Simple train/test splits overestimate performance on small omics datasets. Nested CV provides unbiased estimates by separating hyperparameter tuning from performance evaluation.

Nested Cross-Validation

from sklearn.model_selection import cross_val_score, StratifiedKFold, GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import numpy as np

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', RandomForestClassifier(random_state=42))
])

param_grid = {
    'clf__n_estimators': [50, 100, 200],
    'clf__max_depth': [5, 10, None]
}

## Stratified K-Fold

```python
from sklearn.model_selection import StratifiedKFold, cross_val_score

适用场景

Use bio-machine-learning-model-validation ，用于 genomics 、 bioinformatics workflows。
Apply bio-machine-learning-model-validation to sequencing，variant，或 omics analysis tasks。