Curriculum

Model Evaluation Project

Audit two candidate classifiers and defend a deployment threshold using evidence rather than one headline score.

Not started3 min explanation

Visualize, practice, and deep-dive material are optional—use only what helps you learn.

Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

  • Audit competing model evidence
  • Choose a threshold from explicit costs
  • Write a defensible deployment recommendation

Helpful before starting

  • Data preprocessing
  • ML workflow and metrics
  • Linear and logistic regression basics

Start here

Model Evaluation Project, in plain language

Audit two candidate classifiers and defend a deployment threshold using evidence rather than one headline score. This project consolidates splitting, baselines, metrics, uncertainty, calibration, and threshold decisions into one evidence-based review.

For a small example, two classifiers trade recall for precision on 20 examples. Build a confusion matrix at both thresholds and state which mistake matters more for the decision. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

  • Metric contracts and decision costs.
  • Confusion matrices and threshold sweeps.
  • First-attempt evidence versus post-review improvement.

Use it well

When Model Evaluation Project helps—and where it breaks

A candidate model appears 4% better than baseline. Reproduce the split, verify leakage controls, add confidence intervals, segment errors, and document the chosen operating point. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

  • Picking the model with the largest accuracy. Better approach: Use the metric and threshold tied to the decision.
  • Hiding unfavorable slices. Better approach: Report material variation and sample sizes.
  • Claiming readiness without an operating plan. Better approach: Include monitoring, fallback, and rollback.

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