Prediction, Explanation, and Decision-Making

~26 min · framing, decisions, interpretability

Level 0Scout

0 XP0/48 lessons0/11 achievements

0/120 XP to next level120 XP to go0% complete

Three jobs that get conflated

Stakeholders often ask for a model when they actually want one of three different things: a prediction (what number, what label), an explanation (why this prediction), or a decision (what action). Each job has a different success metric and a different deployment shape.

Why this matters

An accurate predictor can still be a bad decision tool if the threshold is wrong, the action is misaligned, or the user cannot tolerate the false-positive cost. A great explanation can still be a bad decision tool if the explanation does not point to a feasible intervention. A confident decision system without inspection is a liability when it errs.

Designing for the right job

Write the job sentence first: given X, predict Y, then take action Z when probability exceeds threshold T. If the team cannot finish that sentence, you are not ready to model. The threshold and action belong on the design doc, not in the deploy script as a magic number.

Code

Separate the prediction from the decision·python

probs = model.predict_proba(X)[:, 1]

def decide(prob, threshold=0.42, cost_fp=5, cost_fn=50):
    expected_cost_action = cost_fp * (1 - prob)
    expected_cost_skip = cost_fn * prob
    return "act" if expected_cost_action < expected_cost_skip else "skip"

Log enough for explanation later·python

import json, datetime

def log_decision(user_id, features, prob, decision):
    payload = {
        "ts": datetime.datetime.utcnow().isoformat(),
        "user_id": user_id,
        "prob": float(prob),
        "decision": decision,
        "features": features,
        "model_version": "churn-2026-05-03",
    }
    print(json.dumps(payload))

External links

Exercise

Pick one classifier you have or want. Write the cost matrix (FP cost, FN cost) in concrete units. Compute the decision threshold that minimizes expected cost. Compare it to the default 0.5 you have probably been shipping.

Progress

Progress is local-only — sign in to sync across devices.

← PreviousFeatures, Labels, Examples, Datasets Next →Good and Bad ML Use Cases

Spotted a bug or have feedback on this page?Report an Issue

Comments 0

🔔 Reply notifications (sign in)

No comments yet — be the first.