Handoff triage model review project

Lock the action and outcome clock

The desk decides at intake whether to investigate a likely missed handoff. Record what counts as a miss, when the label matures and which crew or scan fields exist before intervention. Partition by site and date so repeated scans cannot bridge training and validation. Keep the final later cohort sealed until model design and threshold are fixed. The split lesson and feature timing form the review boundary.

Compare three distinct inductive assumptions

Nearest neighbors assumes nearby shipment profiles have useful local labels; categorical naive Bayes combines conditional likelihoods; logistic regression fits a linear log-odds surface. Fit any scaling, smoothing choice, feature selector and weights inside development folds. Compare all three with a training-rate baseline. Neighbor support, feature dependence and fold-local selection are separate failure modes.

Build an action scorecard

On the untouched cohort, report prevalence, confusion counts, precision, recall, calibration and daily alert volume. Show slices by site and shift with denominators. Evaluate the actual staffing cost of false alarms against missed handoffs. If the desk chooses among multiple interventions, compare their expected and realized costs rather than choosing the most likely label blindly. The multiclass guide explains that distinction.

Explain why weighting was or was not needed

If the learner ignores the rare class, compare a class-weighted fit with a calibrated unweighted fit using a cost-selected threshold. Recheck probability quality after weighting. Do not claim a training weight equals the business cost ratio. The same future cases must support the comparison. The weighting guide keeps optimization and policy separate.

Record a pilot decision

The code lists evidence required for a limited pilot discussion. A passing checklist does not prove future value; it means a named owner can review the attached measurements, rollback rule and delayed-label monitor. If calibration or capacity is missing, hold the alert workflow and collect the required evidence. The monitoring lesson supplies the post-launch clock.

Implementation

def triage_review(packet):
    requirements = {
        "feature_clock_checked": "prediction-time features",
        "site_time_split_checked": "site and time split",
        "final_cohort_sealed": "final cohort",
        "class_support_reported": "class support",
        "calibration_checked": "calibration",
        "review_capacity_checked": "desk capacity",
        "cost_rule_written": "intervention costs",
        "delayed_label_monitor_ready": "matured-label monitor",
    }
    missing = [description for key, description in requirements.items()
               if not packet.get(key)]
    return "pilot review" if not missing else "hold: " + ", ".join(missing)

dispatch_packet = {
    "feature_clock_checked": True,
    "site_time_split_checked": True,
    "final_cohort_sealed": True,
    "class_support_reported": True,
    "calibration_checked": False,
    "review_capacity_checked": False,
    "cost_rule_written": True,
    "delayed_label_monitor_ready": True,
}
assert triage_review(dispatch_packet) == "hold: calibration, desk capacity"

Performance and operating cost

Brute-force neighbor scoring is O(NF) per case before neighbor selection, while a fitted naive Bayes or logistic score is O(F) per case. The review gate is O(K) for K requirements; it cannot calculate calibration, operational cost or case-review capacity without their underlying data.

Common Mistakes

Do not reuse a final cohort to choose features or a threshold.
Do not interpret naive Bayes or weighted-model scores as calibrated without checking.
Do not launch an alert stream that exceeds the desk’s review capacity.