Evaluating a model honestly: validate, measure and explain
A model is worth what it gets right on data it has not seen, measured with the metric that fits the decision and compared against something. That is exactly what is usually missing from the result a copilot hands you: a lone percentage arrives, with no exam to back it up, no rival and no fine print. This course teaches you to ask for those three things, to read them and to sign off on what comes out.
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| Responsible | Carlos Pascual |
|---|---|
| Last Update | 2026-10-06 |
| Completion Time | 4 hours 40 minutes |
| Members | 1 |
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Intermediate
Data, Analytics and AI
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Preview
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Block 1: Validating without cheating3Lessons · 1 hr 10 min
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The exam it has not seen
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Leaks: when the model sees the answer
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Block 1 exam10 xp
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Block 2: Measuring: the metric is a decision3Lessons · 1 hr 10 min
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Errors in a quantity
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Errors in a yes or a no
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Block 2 exam10 xp
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Block 3: Comparing and explaining3Lessons · 1 hr 10 min
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What to compare against
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Explaining a prediction and checking that it treats everyone alike
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Block 3 exam10 xp
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Block 4: From model to system: evaluating an AI and keeping watch3Lessons · 1 hr 5 min
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From validation to the case battery
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After the exam: drift and sign-off
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Block 4 exam10 xp
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