Define the readiness random forest input contract

The accepted input remains: rows of (hours, practice_tests) and binary 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: stabilize predictions when one noisy training row flips.

from sklearn.ensemble import RandomForestClassifier
clf=RandomForestClassifier(n_estimators=50, random_state=0)
assert clf.n_estimators>=10
print({'n_estimators':clf.n_estimators})

Expected evidence: n_estimators at least 10. 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 random forest so tightening the boundary does not over-reject. Document field names and types the way a teammate would need them on day two of random-forests—not as comments you plan to delete.

List every field in tabular readiness set with one intentionally noisy 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 random forest.

Add one sentence about encoding, units, or timezones if relevant to rows of (hours, practice_tests) and binary readiness labels. Contracts that ignore units create “correct” programs that still ship wrong decisions when someone tries to stabilize predictions when one noisy training row flips.

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 n_estimators=1 disguised as a forest, or leaking the test row into every tree harder to confuse with a model or algorithm bug later.

At the data contract stage for random-forests, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about tabular readiness set with one intentionally noisy 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: single decision-tree accuracy on the same split.

For this page specifically, success looks like malformed inputs rejected with field-named errors while still centering the user decision to stabilize predictions when one noisy training row flips. If you cannot point to a file, command, or assertion that proves that for the readiness random forest, stay on this page instead of advancing.

Random forest glossary

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