Set up interfaces and contracts

The required setup is: an activated environment contains scikit-learn and each feature column has a stable meaning and numeric representation. Confirm it before copying code. The contract separates input from output: rows of [hours, practice_tests] and binary readiness labels goes in, and majority-vote classes, class probabilities, and evaluation evidence from rows not used by individual trees comes out. If either side is ambiguous, later debugging will chase the wrong layer.

The input contract is rows of [hours, practice_tests] and binary readiness labels. The visible result is majority-vote classes, class probabilities, and evaluation evidence from rows not used by individual trees.

from sklearn.ensemble import RandomForestClassifier

X = [[1, 0], [2, 1], [3, 0], [4, 2], [5, 1], [6, 3], [7, 2], [8, 4]]
y = [0, 0, 0, 1, 1, 1, 1, 1]

forest = RandomForestClassifier(
    n_estimators=200, max_depth=3, oob_score=True,
    random_state=7, n_jobs=-1,
)
forest.fit(X, y)
prediction = forest.predict([[5, 2]])
probabilities = forest.predict_proba([[5, 2]])
print("predicted class:", prediction[0])
print("probability shape:", probabilities.shape)
print("probabilities sum to one:", probabilities[0].sum().round(6) == 1)
print("oob in range:", 0 <= forest.oob_score_ <= 1)

Expected evidence:

predicted class: 1
probability shape: (1, 2)
probabilities sum to one: True
oob in range: True

The output may include version-specific details such as hashes, paths, fitted thresholds, or final decimal places. Compare the structural facts described here rather than copying placeholders. If the structure differs, stop and inspect the earliest unexpected line.

1. n_estimators sets the number of independently grown trees whose results are combined.
2. max_depth limits each member's complexity.
3. a fixed random_state makes bootstrap and feature sampling reproducible.
4. predict_proba reports vote fractions by class; out-of-bag scoring uses rows omitted from each tree's bootstrap sample.

These lines form one chain: rows of [hours, practice_tests] and binary readiness labels becomes majority-vote classes, class probabilities, and evaluation evidence from rows not used by individual trees. Change only one input first. When several values change together, you cannot tell which change caused the new behavior.

• First failure: very small datasets can produce unreliable out-of-bag scores and warnings because too few independent votes exist. Re-run the smallest command that proves the repair.
• Second failure: feature order drift still changes predictions even though forests accept the numeric shape. Preserve the failing input as a test when it represents a realistic mistake.
• Misleading success: more trees reduce Monte Carlo variation but cannot correct wrong labels, leakage, or a biased sample. A clean-looking final line cannot cancel contradictory intermediate evidence.

When debugging, copy the exact error text and inspect names, paths, shapes, types, and versions. Explain the cause in one sentence before changing code. That discipline prevents a guessed repair from creating a second defect.

Feature importance is not a causal explanation and can split credit among correlated columns. Protect training data, cap parallel CPU use in shared systems, and monitor both input drift and class-specific errors.

For the current setup step, save the smallest useful evidence: the relevant command, its output, and the input that produced it. Do not use a screenshot as the only record when text can be copied and searched. Keep generated artifacts separate from source inputs so rerunning the example does not destroy the evidence it is meant to evaluate.

Read every command or statement before running it. The examples deliberately expose intermediate state so a surprising result has somewhere concrete to point.

Return to your opening prediction. Mark it correct or rewrite it with the condition you missed. Then explain the difference between a successful execution and a trustworthy result for this specific example.