Scikit Learn

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 & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: ihatesea69
• Source: ihatesea69/kiro-kit
• License: MIT
• Homepage: https://www.npmjs.com/package/kiro-kit

Install and usage instructions live in the source repository linked above.