Honest Gate — Validate honestly

Expose issues early. Validate fairly. Trust what you ship.

Validation prevents surprises in production. Check for leaks, bias, drift, and weak signals before you deploy.

Live on

audited for real on your data

Kernel asleep

Feature	\|corr\|	Risk

A single feature that correlates almost perfectly with the target is often leakage. This is the intuition behind audit().

OverviewLeakageFairnessDriftSignificanceModels

Churn v1 (Test) ⌄

Export

Leakage check

Potential leakage detected

3 features may contain target leakage or post-outcome information.

Feature	Risk
days_since_last_contact Occurs after churn	0.92
support_tickets_30d Leakage window	0.81
churn_flag Direct target proxy	0.99

Within thresholdExceeds 0.10

0.24Overall drift (PSI)

Moderate drift detected vs training data.

Model A vs Model B (XGBoost vs LightGBM)

0.028Significant

At alpha = 0.05, the performance difference is statistically significant.

Metric	A	B
ROC AUC	0.912	0.907
Diff	+0.005

XGBoost · #2

ROC AUC

0.912

Accuracy

0.876

F1

0.875

Time (s)

0.74

Intended use

Predict customer churn in subscription business to support retention outreach.

Training data

Churn v1 (Train), 2025-04-01 to 2025-05-31.

Next: cross the Bridge. An honest model deserves a clean path to production.