Instrument the readiness decision tree

Instrument the readiness decision tree on hours × practice tests so a run records enough structure to debug offline: counts, latency if relevant, pass/fail of held-out accuracy plus a tree text dump that mentions both features, and a stable stage name. Redact secrets and raw credentials from every event.

import json
print(json.dumps({'model':'DecisionTree','max_depth':2,'features':['hours','practice_tests'],'holdout_acc':1.0}))

Expected evidence: tree metrics JSON. Prefer JSON or structured text you can grep in CI over prose logs for decision-trees.

Turn one historical failure—especially overfit tree with max_depth unrestricted on tiny data—into a test that fails if the signal disappears for the readiness decision tree. Observability without a failing test is optional decoration; observability with a test is part of the decision-trees artifact.

Draft a three-field event for the readiness decision tree: stage, ok, and one domain field derived from held-out accuracy plus a tree text dump that mentions both features. Add fixture_id or docs_version when content can change. Explicitly list fields that must never appear (tokens, passwords, raw prompts) because training on labels that embed sensitive attributes without documenting them is in scope for this lab.

Wire one assertion that fails if the readiness decision tree event is missing after a run. Observability that cannot fail a test will not survive contact with a busy decision-trees repository.

Imagine a teammate opens only your event stream after a bad deploy. Could they tell whether small hours/practice_tests readiness table with one holdout row was wrong, whether overfit tree with max_depth unrestricted on tiny data, or missing feature names in export returned, or whether training on labels that embed sensitive attributes without documenting them slipped through? If not, rename fields until those three stories are distinguishable.

At the testing and observability 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 a structured event schema locked by a test 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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