tomevault-io Scikit-learn
Activate this skill when working with classical ML algorithms.
When to Use
- Building classification or regression models
- Feature engineering and selection
- Implementing ML pipelines with preprocessing
- Cross-validation and hyperparameter tuning
- Clustering and dimensionality reduction
Patterns
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
preprocessor = ColumnTransformer([
("num", StandardScaler(), numeric_features),
("cat", OneHotEncoder(handle_unknown="ignore"), categorical_features),
])
pipeline = Pipeline([
("preprocessor", preprocessor),
("classifier", GradientBoostingClassifier(n_estimators=200)),
])
scores = cross_val_score(pipeline, X, y, cv=5, scoring="f1_macro")
Rules
- Always split data before any preprocessing
- Use pipelines to prevent data leakage
- Cross-validate before reporting metrics
- Start simple (LogisticRegression) before complex models
- Document feature engineering decisions
Source: ihatesea69/kiro-kit — distributed by TomeVault.