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If the P k of a weapon/target engagement is 30% (or 0.30), then every random number generated that is less than 0.3 is considered a "kill"; every number greater than 0.3 is considered a "no kill". When used many times in a simulation, the average result will be that 30% of the weapon/target engagements will be a kill and 70% will not be a kill.
Social media reach is a media analytics metric that refers to the number of users who have come across a particular content on a particular social media platform. [1] Social media platforms have their own individual ways of tracking, analyzing and reporting the traffic on each of the individual platforms.
Engagement rate, a performance metric that measures the quality of social media activity such as Facebook likes or Twitter retweets, can be interpreted in terms of "engagement per follower," measured by dividing the raw counts of social media activity by the number of followers. [9]
Conversion rate optimization seeks to increase the percentage of website visitors that take a specific action (often submitting a web form, making a purchase, signing up for a trial, etc.) by methodically testing alternate versions of a page or process [citation needed], and through removing impediments to user experience and improving page loading speeds.
Click-through rate (CTR) is the ratio of clicks on a specific link to the number of times a page, email, or advertisement is shown. It is commonly used to measure the success of an online advertising campaign for a particular website, as well as the effectiveness of email campaigns. [1] [2] Click-through rates for ad campaigns vary tremendously.
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A view-through rate (VTR), measures the number of post-impression response or viewthrough from display media impressions viewed during and following an online advertising campaign. Such post-exposure behavior can be expressed in site visits, on-site events, conversions occurring at one or more Websites or potentially offline:
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).