Define the tiny linear model input contract

The accepted input remains: training pairs of hours and scores plus one untouched test pair. Keep parsing and normalization in functions that do not score, train, or call a model. That split lets a test fail the boundary without blaming the core logic. The user-facing decision stays: estimate slope/intercept from study hours and score a held-out row honestly.

def split_ok(train, holdout):
    assert holdout not in train
print(split_ok([(1,2),(2,4)],(5,8)) or 'holdout excluded')

Expected evidence: holdout excluded. If the contract is silent on a bad value, later debugging will look like an algorithm bug when it is really a data bug.

Write one assertion for a neighboring valid input to the tiny linear model so tightening the boundary does not over-reject. Document field names and types the way a teammate would need them on day two of train-a-tiny-model—not as comments you plan to delete.

List every field in small hours/score table with one held-out pair and mark each as required, optional, or forbidden. Required fields must fail loudly when missing; optional fields need defaults you can quote in a test; forbidden fields (secrets, raw PII, path escapes) must never be accepted silently. This list is the contract for the tiny linear model.

Add one sentence about encoding, units, or timezones if relevant to training pairs of hours and scores plus one untouched test pair. Contracts that ignore units create “correct” programs that still ship wrong decisions when someone tries to estimate slope/intercept from study hours and score a held-out row honestly.

Write the error string you want for the most likely bad input. Prefer ValueError('threshold out of range')-style messages over generic invalid input. The contract’s job is to make training on the test row, or reporting R² without the holdout error harder to confuse with a model or algorithm bug later.

At the data contract 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 malformed inputs rejected with field-named errors 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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