scikit-learn-specialist
Master scikit-learn machine learning patterns including pipeline design, cross-validation, hyperparameter tuning, feature engineering, and model evaluation. Use PROACTIVELY when building ML models, evaluating classifiers/regressors, or designing ML workflows.
npx skills add majiayu000/claude-skill-registry --skill scikit-learn-specialist --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Scikit-Learn Specialist Master machine learning workflows with scikit-learn, focusing on proper pipeline design, validation, and avoiding common pitfalls. ## When to Use This Skill - Building classification or regression models - Feature engineering and preprocessing - Model selection and hyperparameter tuning - Cross-validation and evaluation - Creating reproducible ML pipelines - Deploying trained models ## Quick Reference ```python from sklearn.model_selection import train_test_split, cross_val_score from sklearn.preprocessing import StandardScaler from sklearn.pipeline import make_pipeline from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report # Basic workflow X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = make_pipeline(StandardScaler(), RandomForestClassifier()) model.fit(X_train, y_train) print(classification_report(y_test, model.predict(X_test))) ``` --- ## Data Splitting ### Proper Train/Test Split ```python from sklearn.model_selection import train_test_split # ✅ Always split BEFORE any preprocessing X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, r
- When to Use This Skill
- Quick Reference
- Data Splitting
- Proper Train/Test Split
- Train/Validation/Test Split
- Time Series Split
- Pipelines
- Why Use Pipelines
- Building Complex Pipelines
- Feature Union for Multiple Feature Sets
- Cross-Validation
- Basic Cross-Validation
- Multiple Metrics
- Stratified K-Fold (Imbalanced Data)
What does the scikit-learn-specialist skill do?
Master scikit-learn machine learning patterns including pipeline design, cross-validation, hyperparameter tuning, feature engineering, and model evaluation. Use PROACTIVELY when building ML models, evaluating classifiers/regressors, or designing ML workflows.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scikit-learn-specialist --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.