Build the first working readiness random forest

Build only what the claim requires: majority-vote class, class probability, and evaluation on unused rows are printed. Prefer boring, deterministic code over frameworks you cannot yet explain. Run the path twice; identical output on this fixture is a feature, not a lack of creativity.

from sklearn.ensemble import RandomForestClassifier
X=[[1,0],[2,1],[4,1],[5,2],[3,0],[6,2]]; y=[0,0,1,1,0,1]
clf=RandomForestClassifier(n_estimators=50, random_state=0).fit(X[:-1],y[:-1])
print(clf.predict([X[-1]])[0], clf.predict_proba([X[-1]])[0].round(3).tolist())

Expected evidence: class and probability vector. Read each printed intermediate as part of the argument that the path works—not as decoration.

Narrate the journey from raw input to result for a single example from tabular readiness set with one intentionally noisy row. If you cannot name an intermediate, the implementation is still too opaque for this lab. Only after this path is solid should you generalize data sources or UI.

While implementing the readiness random forest, print or log at least three intermediates that map to the claim (majority-vote class, class probability, and evaluation on unused rows are printed). Good intermediates are values a teammate could recompute with a calculator or diff. Bad intermediates are framework traces you cannot explain.

Re-run with tabular readiness set with one intentionally noisy row twice. If the second run differs, either the path is nondeterministic (document the seed) or you have hidden global state—both are lab bugs until named.

Stop adding features once the path supports stabilize predictions when one noisy training row flips. Extra UI, extra tools, or extra models belong in later chapters. The mastery bar for this page is simply: a deterministic end-to-end path with intermediates.

At the implementation 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 a deterministic path with printed intermediates 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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