Measure whether the tiny linear model works

Translate the claim into assertions or a tiny eval harness. The metric to protect is: holdout absolute error; slope sign matches the hours/score trend. Always record the denominator (how many cases) beside any rate. A percentage without a denominator is marketing, not measurement.

import numpy as np
X=np.array([1.0,2,3,4]); y=np.array([2.0,4,5,7])
slope,_=np.polyfit(X,y,1)
assert slope>0
print('slope positive as expected')

Expected evidence: slope positive as expected. A passing assertion proves only the behavior it names; broader usefulness still needs the chapter’s full limits.

Be explicit: beating the baseline (predict mean train score for the holdout before fitting) on this fixture does not prove behavior under training on the test row, or reporting R² without the holdout error. Label observations separately from conclusions so the next page inherits honest evidence about the tiny linear model.

Compute holdout absolute error; slope sign matches the hours/score trend with the denominator written beside the rate every time. For this chapter, the evaluation set is intentionally tiny; that is allowed only if you say so in the evidence. Compare against predict mean train score for the holdout before fitting before celebrating.

Add one negative case aimed at training on the test row, or reporting R² without the holdout error. A suite with only happy cases cannot protect the tiny linear model when the characteristic failure appears in review.

If a check is expensive or flaky, shrink it until it is deterministic on small hours/score table with one held-out pair. Flaky green builds teach the team to ignore gates. Record what this page does not prove so security-ops and mastery-ship inherit honest limits.

At the evaluation stage for train-a-tiny-model, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about small hours/score table with one held-out pair that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: predict mean train score for the holdout before fitting.

For this page specifically, success looks like metrics with explicit denominators and a negative case while still centering the user decision to estimate slope/intercept from study hours and score a held-out row honestly. If you cannot point to a file, command, or assertion that proves that for the tiny linear model, stay on this page instead of advancing.

Overfitting glossary

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Return to the train a tiny model scenario and restate what this chapter proved on page validation.mdx. Name one metric, one ownership rule, and one regression test you will keep. Explain how this page connects to the previous page without repeating earlier paragraphs. If you cannot name a falsifier, the chapter is still a story rather than a controlled practice. Write the falsifier as an observable event with a threshold.

Return to the train a tiny model scenario and restate what this chapter proved on page validation.mdx. Name one metric, one ownership rule, and one regression test you will keep. Explain how this page connects to the previous page without repeating earlier paragraphs. If you cannot name a falsifier, the chapter is still a story rather than a controlled practice. Write the falsifier as an observable event with a threshold.

Return to the train a tiny model scenario and restate what this chapter proved on page validation.mdx. Name one metric, one ownership rule, and one regression test you will keep. Explain how this page connects to the previous page without repeating earlier paragraphs. If you cannot name a falsifier, the chapter is still a story rather than a controlled practice. Write the falsifier as an observable event with a threshold.