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Since reach is a time-dependent summary of aggregate audience behavior, reach figures are meaningless without a period associated with them: an example of a valid reach figure would be to state that "[example website] had a one-day reach of 1565 per million on 21 March 2004" (though unique users, an equivalent measure, would be a more typical ...
Other examples of factors that can impede the reach can include the time at which posts are made, as well as how frequent the posts are between one another. [ 1 ] In comparison, an impression is the total number of circumstances where content has been shown on a social timeline, [ 1 ] Meanwhile, engagement looks at how people interact with the ...
For example, a service-based business could use these calculations: Recency = 10 – the number of months that have passed since the customer last purchased [ 2 ] Frequency = the maximum of "the number of purchases by the customer in the last 12 months (with a limit of 10)" and 1
In asymptotic analysis in general, one sequence () that converges to a limit is said to asymptotically converge to with a faster order of convergence than another sequence () that converges to in a shared metric space with distance metric | |, such as the real numbers or complex numbers with the ordinary absolute difference metrics, if
The rule can then be derived [2] either from the Poisson approximation to the binomial distribution, or from the formula (1−p) n for the probability of zero events in the binomial distribution. In the latter case, the edge of the confidence interval is given by Pr( X = 0) = 0.05 and hence (1− p ) n = .05 so n ln (1– p ) = ln .05 ≈ −2.996.
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).
A famous story about association rule mining is the "beer and diaper" story. A purported survey of behavior of supermarket shoppers discovered that customers (presumably young men) who buy diapers tend also to buy beer. This anecdote became popular as an example of how unexpected association rules might be found from everyday data.
where the antecedent is the input variable that we can control, and the consequent is the variable we are trying to predict. Real mining problems would typically have more complex antecedents, but usually focus on single-value consequents. Most mining algorithms would determine the following rules (targeting models): Rule 1: A implies 0