Use instrumentation, simple baseline, representative examples, and stable labels to make a better ML project decision.
What is the strongest next move for Should This Be Machine Learning Yet?? Write instrumentation, simple baseline, representative examples, and stable labels first, then choose the simplest testable modeling step. Use the most powerful model available so performance can overcome messy setup. Keep the default metric and threshold because defaults are neutral. Delay all work until perfect data exists. The strong answer follows Google Rules of Machine Learning by making the decision evidence explicit before optimizing. Right: it creates evidence and a baseline before complexity. Weaker: complexity amplifies unclear labels, metrics, or leakage. Weaker: defaults may violate the workflow constraint…
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