The outcome of market basket analysis is measured by which metrics?

Market basket analysis is a data mining technique used to uncover patterns in purchase behavior by analyzing the co-occurrence of items purchased together. The primary metrics that define the outcomes of this analysis are support, confidence, and lift.

Support measures the frequency with which items appear together in transactions, providing insight into how often a particular combination of items is purchased. Confidence indicates the likelihood of purchasing a second item given that the first item has been purchased. This helps in understanding the strength of the association between items. Lift, on the other hand, assesses how much more often the two items are purchased together compared to what would be expected if they were statistically independent. A lift greater than 1 indicates a strong positive association, suggesting that the purchase of one item increases the likelihood of purchasing the other.

Together, these metrics provide a comprehensive view of item associations, helping businesses make informed decisions regarding product placement, promotions, and inventory management based on consumer habits. The other options do not capture the specific metrics used in market basket analysis: the second option lacks direct relevance to transactional data, the third mixes categorical labels without specifying measurable metrics, and the fourth is more about the process rather than the specific measures of association.