Data that was used to build the predictive model as well as illustrating the features that will be used to make its predictions.
Examples you want more of when building a model
Examples you want less of when building a model
Performance, usually expressed as to how much better a predicted result will be than a random result
The general population of the eligible
A measure of how frequently a classifier predicts false positives
Features of importance
These represent a measure of how much of an impact a particular feature made on the model’s predictions: highly important features are used more often and separate the training set more, while unimportant features are used infrequently.
A group of individuals who have the necessary criteria to achieve an outcome
Example: Homeownership is a criteria required to consider purchasing rooftop solar
The eligible audience represents the effective marketable population
When first-party data is used in the decision process for models, not just training but applying first-party data
A process used to adjust for geographic bias in training data.
Your customer data
A quantifiable business goal that can be represented by a group of people who have achieved it
Example: You want to find more customers
The way we get to achieve the outcome
Example: You want to find more customers, so we create a strategy that trains a model to look at historical examples of when your customers became customers
A method of ranking every eligible individual from most to least likely to convert
Example: the output of the strategy that supports the overall outcome of finding more customers, the models scores those to identify those who are likely to become a customer