The article explores the optimality of the simple Bayesian classifier under zero-one loss, a measure commonly used in classification tasks. Despite the classifier's reliance on the assumption of attribute independence, empirical evidence suggests that it performs well in many domains with clear attribute dependencies. The authors derive conditions for the classifier's local and global optimality, showing that it can still be optimal even when the independence assumption is violated. They demonstrate that the region of optimality for the Bayesian classifier under zero-one loss is significantly larger than that under squared error loss, indicating a broader range of applicability. The article also discusses the classifier's limitations, such as its inability to handle certain concept classes like m-of-n concepts, and provides conditions for its global optimality in nominal and numeric domains. Overall, the findings suggest that the Bayesian classifier's performance is more robust and versatile than previously thought, making it a valuable tool in various classification tasks.The article explores the optimality of the simple Bayesian classifier under zero-one loss, a measure commonly used in classification tasks. Despite the classifier's reliance on the assumption of attribute independence, empirical evidence suggests that it performs well in many domains with clear attribute dependencies. The authors derive conditions for the classifier's local and global optimality, showing that it can still be optimal even when the independence assumption is violated. They demonstrate that the region of optimality for the Bayesian classifier under zero-one loss is significantly larger than that under squared error loss, indicating a broader range of applicability. The article also discusses the classifier's limitations, such as its inability to handle certain concept classes like m-of-n concepts, and provides conditions for its global optimality in nominal and numeric domains. Overall, the findings suggest that the Bayesian classifier's performance is more robust and versatile than previously thought, making it a valuable tool in various classification tasks.