Target Leakage Detection

Detect data leakage that would inflate model performance during development but fail in production.

Detection Methodology

1. Temporal Leakage

For each feature, verify it would be available at prediction time:

• Features derived from future events (e.g., outcome_date when predicting outcome)
• Aggregations that include the prediction period
• Features updated after the target was determined

Check: For each feature, ask: "At the moment we need to make a prediction, would this value already be known?" If no, it's leakage.

2. Direct Leakage

Check for features that are transformations of the target:

• Features that are downstream effects of the target (e.g., cancellation_reason when predicting churn)
• One-to-one mappings with the target
• Encoded versions of the target (e.g., revenue_bucket when predicting revenue)

Check: If removing this feature drops model performance by >50%, it may be a proxy for the target.

3. Statistical Signals

Flag when any of these occur:

Signal	Classification Threshold	Regression Threshold
Single feature AUC	> 0.95	N/A
Single feature R-squared	N/A	> 0.95
Feature importance dominated by 1 feature	>50% of total importance	>50% of total importance
Train and test performance nearly identical	Gap < 0.5%	Gap < 0.5%

Check: Run single-feature models. Any feature with AUC > 0.95 or R-squared > 0.95 warrants investigation.

4. Group Leakage

Check for information leaking across the train/test boundary:

• Same entity (customer, patient) appearing in both train and test
• Preprocessing (scaling, encoding) fit on combined train+test data
• Target encoding computed on the full dataset instead of just training folds

Check: Verify that train_ids.intersection(test_ids) is empty for all entity identifiers.

Remediation

For each detected leakage:

1. Describe the mechanism -- How is future/target information flowing into the feature?
2. Assess impact -- What happens to model performance if the feature is removed?
3. Suggest fix:
   • Remove the feature entirely
   • Adjust the time window (e.g., use only data before prediction point)
   • Fix the split strategy (group-aware splits)
   • Fix preprocessing (fit only on training data)

Quick Check Script

import pandas as pd
from sklearn.metrics import roc_auc_score

def check_leakage(df, target_col, feature_cols):
    """Flag features with suspiciously high single-feature AUC."""
    results = []
    for col in feature_cols:
        if df[col].dtype in ['float64', 'int64']:
            try:
                auc = roc_auc_score(df[target_col], df[col])
                auc = max(auc, 1 - auc)  # Handle inverse correlation
                if auc > 0.95:
                    results.append({'feature': col, 'auc': auc, 'risk': 'HIGH'})
                elif auc > 0.85:
                    results.append({'feature': col, 'auc': auc, 'risk': 'MEDIUM'})
            except Exception:
                pass
    return pd.DataFrame(results).sort_values('auc', ascending=False)