Has two modes of operation that can be used to determine how unique one or more attribute is with respect to the general population.

In its first mode of operation (Normalize into Fraction of Total: Yes), Specificity Of can automatically compute the normalized specificity without needing to configure additional features (that contain the approx total count of and perform the divide operation to normalize the value). This is provided as a convenience function for cases where they underlying values are not required.

In its second mode of operation (Normalize into Fraction of Total: No), Specificity Of calculates a high accuracy estimation of the number of times that a group of Identifier/Subject attributes with the same value as the current event occured over the given time period. This figure can be divided into the Approx Total Count of the same attributes in order to calculate specificity of the attributes in the range 0..1 (therefore normalizing the value). This can be useful when both the approximate velocity, and the specificity are required to be used in other more complex models or calculations.
For Example:
- Determining if the value of one or more attributes is an outlier.
- Determining if the value of one or more attributes is normal.
Configuration
- Feature Name
The "dictionary" name under which the feature value will be stored. - Attributes - selects which attribute(s) to measure the unique value/combination(s) of
- Any attribute or combination of attributes may be selected.
- Default Value
A value that can be assigned to the feature if the attributes to calculate the features are not present. - Scope
Can be used to extend the event search from the local node, to all nodes within the same organization all the way to all across customers (global). - Time Window
Can be used to limit the Feature calculation to a specific time period/window. e.g 1 week.
There is no restriction on Identifier or Subject attributes - any attribute(s) may be selected for use in Specificity Of.
Use Case 1
Using Specificity Of to determine if the operating system detected by Javascript profiling is an outlier with respect to the general population.
Implementing In The Feature Editor


- name: spec_js_os_month
default_value: 0
time_window:
months: 1
find_the:
specificity_of:
attributes:
- profiling.javascript.os
event_type: all
normalize: true
Using within Rules
outcome['CHAMPION'].features.general['spec_js_os_month'] <= 0.01
