supervised-learning
Build production-ready classification and regression models with hyperparameter tuning
Profile →npx skills add majiayu000/claude-skill-registry --skill supervised-learning --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.
# Supervised Learning Skill > Build, tune, and evaluate classification and regression models. ## Quick Start ```python from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import cross_val_score, train_test_split # Split data X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, stratify=y, random_state=42 ) # Train model model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train) # Evaluate cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='f1_weighted') print(f"CV F1: {cv_scores.mean():.4f} (+/- {cv_scores.std()*2:.4f})") print(f"Test Accuracy: {model.score(X_test, y_test):.4f}") ``` ## Key Topics ### 1. Classification Algorithms | Algorithm | Best For | Complexity | |-----------|----------|------------| | **Logistic Regression** | Baseline, interpretable | O(n*d) | | **Random Forest** | Tabular, general | O(n*d*trees) | | **XGBoost** | Competitions, accuracy | O(n*d*trees) | | **SVM** | High-dim, small data | O(n²) | ```python from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier from xgboost import XGBClassifier classifiers = { 'lr
- Quick Start
- Key Topics
- 1. Classification Algorithms
- 2. Regression Algorithms
- 3. Hyperparameter Tuning
- 4. Handling Class Imbalance
- 5. Model Comparison
- Best Practices
- DO
- DON'T
- Exercises
- Exercise 1: Model Selection
- Exercise 2: Hyperparameter Tuning
- Unit Test Template
What does the supervised-learning skill do?
Build production-ready classification and regression models with hyperparameter tuning
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill supervised-learning --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.