Probability and Bayes

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1. Which explanation matches prior beliefs and new evidence?A. Bayes' rule relates a hypothesis after evidence to the evidence's likelihood and the prior.B. Naive Bayes combines feature likelihoods while assuming conditional independence given the class.C. Calibration asks whether predicted frequencies match observed frequencies on held-out data.D. A prior expresses an initial probability before the new evidence is considered.

2. Which explanation matches bayes' rule with a worked example?A. A prior expresses an initial probability before the new evidence is considered.B. Naive Bayes combines feature likelihoods while assuming conditional independence given the class.C. Bayes' rule relates a hypothesis after evidence to the evidence's likelihood and the prior.D. Calibration asks whether predicted frequencies match observed frequencies on held-out data.

3. Which explanation matches naive bayes and its assumptions?A. A prior expresses an initial probability before the new evidence is considered.B. Naive Bayes combines feature likelihoods while assuming conditional independence given the class.C. Bayes' rule relates a hypothesis after evidence to the evidence's likelihood and the prior.D. Calibration asks whether predicted frequencies match observed frequencies on held-out data.

4. For bayes' rule with a worked example, which result or action fits this practical case: A weather alert catches many late deliveries but also some on-time ones.A. With a 10% late rate, 80% sensitivity, and 20% false alarms, positive alerts imply about 31% late risk.B. Update a low base rate using a weather signal rather than treating that signal alone as the answer.C. Try it as a baseline and compare performance on correlated route and distance inputs.D. Group validation predictions near 0.7 and compare with their actual late fraction.

5. For check whether probabilities are calibrated, which result or action fits this practical case: Among deliveries given a 70% late risk, far fewer are actually late.A. Update a low base rate using a weather signal rather than treating that signal alone as the answer.B. With a 10% late rate, 80% sensitivity, and 20% false alarms, positive alerts imply about 31% late risk.C. Try it as a baseline and compare performance on correlated route and distance inputs.D. Group validation predictions near 0.7 and compare with their actual late fraction.

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Probability and Bayes

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