Model Evaluation Project
Audit two candidate classifiers and defend a deployment threshold using evidence rather than one headline score.
Concept overview
What this lesson will help you understand.
This project consolidates splitting, baselines, metrics, uncertainty, calibration, and threshold decisions into one evidence-based review.
Learn by doing
Explore the concept.
Make a prediction before changing a control. Run the experiment, explain what moved, and compare the result with the theory below.
Initializing Interactive Playground...
Complete lesson
Detailed concept deep dive.
Build intuition first, then work through implementation, mathematical derivations, failure analysis, and real system decisions.
6 guided chapters
This project consolidates splitting, baselines, metrics, uncertainty, calibration, and threshold decisions into one evidence-based review.
Before working through the formal derivations, connect the vocabulary to one small, concrete example. The goal is not to memorize definitions in isolation. It is to understand what each idea represents, which assumptions make it valid, and how the pieces relate to one another.
Ideas to understand
- Metric contracts and decision costs
- Confusion matrices and threshold sweeps
- First-attempt evidence versus post-review improvement
Learn by doing
Make the idea concrete
Explain the model evaluation project with a concrete example and no unexplained jargon.
Try this
- Define “Metric contracts and decision costs” in your own words, then annotate one concrete Model Evaluation Project input and output.
- Construct one valid case and one counterexample for “Confusion matrices and threshold sweeps”; explain which assumption separates them.
- Predict how “First-attempt evidence versus post-review improvement” will change one visible playground result, then test and record the before/after values.
Evidence of understanding
- You can explain the example without relying on jargon.
- You can name the assumptions and identify what would invalidate them.
- You can connect the example to at least one real ML use case.
What part of the Model Evaluation Project mental model still feels least intuitive, and what example would help clarify it?
Saved in your browser progress and included in exports.
Practice with code
Make the idea executable.
Complete the starter code, use progressive hints, and pass deterministic checks in the browser.
Runnable Python lab
Audit two candidate classifiers
Compute the lowest-cost feasible candidate-threshold pair under an explicit false-negative cost and review-capacity constraint.
Keep learning
Papers, standards, and practical references.
Start with the free primary sources. Books are included where a longer, connected treatment is worth the investment.
Test your understanding.
Answer explanations appear after every choice. Missed questions can be reviewed before a full retake.
Mastery requires 80% or higher.
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