Define the readiness decision tree input contract

The accepted input remains: numeric rows (hours, practice_tests) with readiness labels. 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: predict readiness with a path a human can read aloud.

def row_ok(r):
    assert len(r)==2 and all(isinstance(v,(int,float)) for v in r)
row_ok([3,1]); print('row contract ok')

Expected evidence: row contract ok. 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 readiness decision tree so tightening the boundary does not over-reject. Document field names and types the way a teammate would need them on day two of decision-trees—not as comments you plan to delete.

List every field in small hours/practice_tests readiness table with one holdout row 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 readiness decision tree.

Add one sentence about encoding, units, or timezones if relevant to numeric rows (hours, practice_tests) with readiness labels. Contracts that ignore units create “correct” programs that still ship wrong decisions when someone tries to predict readiness with a path a human can read aloud.

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 overfit tree with max_depth unrestricted on tiny data, or missing feature names in export harder to confuse with a model or algorithm bug later.

At the data contract stage for decision-trees, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about small hours/practice_tests readiness table with one holdout row 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: majority-class accuracy on the holdout before fitting the tree.

For this page specifically, success looks like malformed inputs rejected with field-named errors while still centering the user decision to predict readiness with a path a human can read aloud. If you cannot point to a file, command, or assertion that proves that for the readiness decision tree, stay on this page instead of advancing.

Decision tree glossary

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