Explain bias-variance, calibration, and model drift

Q: Explain bias-variance, calibration, and model drift

This question evaluates a candidate's grasp of core machine learning fundamentals—bias–variance trade-off, probability calibration, and model drift—and the competency to map statistical model behavior to business-facing needs like stable decisions and calibrated probabilities.

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Question

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You are interviewing for an applied ML role. Answer the following ML fundamentals questions in a business-facing way (i.e., start from a customer/business need, then map it to the ML concept and actions).

1) Bias–variance trade-off

Define bias and variance and explain the trade-off.
Give practical ways to reduce bias .
Give practical ways to reduce variance .

2) Confidence (probability) calibration

Define confidence calibration (what does it mean for a model to be calibrated?).
How would you measure calibration?
What are common methods to improve calibration?

3) Model drift

Define model drift (and distinguish common types of drift).
How would you detect drift in production?
What are typical mitigation actions once drift is detected?

Assume a typical product scenario (e.g., ranking/recommendation, fraud detection, churn prediction, ads CTR) where the customer wants stable, reliable decisions and probabilities over time.

Explain bias-variance, calibration, and model drift

Quick Overview

1) Bias–variance trade-off

2) Confidence (probability) calibration

3) Model drift

Solution

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