Measure whether the readiness decision tree works

Translate the claim into assertions or a tiny eval harness. The metric to protect is: held-out accuracy plus a tree text dump that mentions both features. Always record the denominator (how many cases) beside any rate. A percentage without a denominator is marketing, not measurement.

from sklearn.tree import DecisionTreeClassifier
X=[[1,0],[2,1],[4,1],[5,2]]; y=[0,0,1,1]
clf=DecisionTreeClassifier(max_depth=2, random_state=0).fit(X[:3], y[:3])
acc=(clf.predict(X[3:])==y[3:]).mean(); print({'holdout_acc':float(acc)})

Expected evidence: holdout_acc value. A passing assertion proves only the behavior it names; broader usefulness still needs the chapter’s full limits.

Be explicit: beating the baseline (majority-class accuracy on the holdout before fitting the tree) on this fixture does not prove behavior under overfit tree with max_depth unrestricted on tiny data, or missing feature names in export. Label observations separately from conclusions so the next page inherits honest evidence about the readiness decision tree.

Compute held-out accuracy plus a tree text dump that mentions both features 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 majority-class accuracy on the holdout before fitting the tree before celebrating.

Add one negative case aimed at overfit tree with max_depth unrestricted on tiny data, or missing feature names in export. A suite with only happy cases cannot protect the readiness decision tree when the characteristic failure appears in review.

If a check is expensive or flaky, shrink it until it is deterministic on small hours/practice_tests readiness table with one holdout row. 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 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 metrics with explicit denominators and a negative case 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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For decision-trees, write a transfer example that differs in one constraint from the chapter scenario. Keep the quality bar fixed. Explain which check still applies.