Scikit Learn

scikit-learn Tabular ML AI Skill Guide

Overview & Engine Architecture

scikit-learn standardizes estimators with fit / predict / transform. Pipeline and ColumnTransformer bundle preprocessing with models so cross-validation applies the same folds to transforms. Agents prevent leakage by fitting preprocessors inside CV, choose metrics matching the task, and persist entire pipelines - not only the final classifier.

raw columns -> ColumnTransformer
                    -> Pipeline(estimator)
                    -> cross_val / GridSearchCV
                    -> joblib dump

When to use this skill

• Classical ML on tabular features
• Baselines before deep learning
• Feature preprocessing that must ship with the model

Operational directives

1. Never fit scalers/encoders on the full dataset before splitting.
2. Use Pipeline so GridSearchCV tunes preprocessing + model together.
3. Stratify classification splits when classes are imbalanced.
4. Report confidence intervals / multiple seeds for small data.
5. Persist with joblib.dump(pipeline, ...) including transforms.

Pipeline example

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import HistGradientBoostingClassifier

num = Pipeline([("imp", SimpleImputer()), ("sc", StandardScaler())])
cat = Pipeline([
    ("imp", SimpleImputer(strategy="most_frequent")),
    ("oh", OneHotEncoder(handle_unknown="ignore")),
])

pre = ColumnTransformer([
    ("num", num, ["amount", "tenure_days"]),
    ("cat", cat, ["segment", "region"]),
])

clf = Pipeline([
    ("pre", pre),
    ("model", HistGradientBoostingClassifier(max_depth=6)),
])

scores = cross_val_score(clf, X, y, cv=5, scoring="roc_auc")
print(scores.mean(), scores.std())
clf.fit(X, y)

Persist

import joblib
joblib.dump(clf, "artifacts/churn_pipeline.joblib")
pipe = joblib.load("artifacts/churn_pipeline.joblib")
pipe.predict_proba(X_new)[:, 1]

Common failures

Best practices

• Start with DummyClassifier / simple linear baselines.
• Align scoring with business cost (precision@k, recall, MAE).
• Keep train/serve features identical via the saved pipeline.
• Log params/metrics with @mlflow.

Limitations

• Not ideal for raw text/image end-to-end (use @huggingface-transformers, @pytorch).
• GPU acceleration is limited compared to deep learning stacks.
• Online learning APIs are partial - batch retrain is the common path.

Related skills

• @pandas - feature tables
• @mlflow - experiment tracking
• @great-expectations - validate feature inputs