ml-data-leakage-guard

ML Data Leakage Guard Skill

Automatically detects and prevents data leakage in machine learning workflows by verifying that all preprocessing steps, feature engineering, and statistical computations would be available at prediction time.

When to Use This Skill

Use this skill after work involving:

• Data preprocessing (normalization, standardization, scaling)
• Missing value imputation
• Feature engineering and feature selection
• Dimensionality reduction (PCA, SVD, t-SNE)
• Target encoding or label encoding
• Time series feature construction
• Data augmentation strategies
• Algorithm development and optimization
• Train-test split procedures
• Cross-validation setup

Not For / Boundaries

• Pure theoretical ML discussions without implementation
• Model architecture design (without data preprocessing)
• Hyperparameter tuning (unless it involves data-dependent operations)

Core Principle

The Golden Rule: At the exact moment of prediction in production, can I access this value from the database or compute it using only information available up to that point?

If the answer is "no" or "not completely", then data leakage exists.

Quick Reference

Critical Leakage Patterns

Pattern 1: Preprocessing Before Split

# ❌ WRONG: Leakage - fit on entire dataset
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)  # Uses test set statistics
X_train, X_test = train_test_split(X_scaled, y)

# ✅ CORRECT: Fit only on training data
X_train, X_test, y_train, y_test = train_test_split(X, y)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)  # Fit on train only
X_test_scaled = scaler.transform(X_test)  # Transform test using train statistics

Pattern 2: Global Missing Value Imputation

# ❌ WRONG: Uses global statistics including test set
df['age'].fillna(df['age'].mean(), inplace=True)  # Global mean includes test data
X_train, X_test = train_test_split(df, y)

# ✅ CORRECT: Compute statistics on training set only
X_train, X_test, y_train, y_test = train_test_split(df, y)
train_mean = X_train['age'].mean()  # Only from training data
X_train['age'].fillna(train_mean, inplace=True)
X_test['age'].fillna(train_mean, inplace=True)  # Use train mean for test