The Detected Target Curve
Determine the percentage of the population to contact to reach a specific percentage of the actual positive target.
The Detected Curve compares your predictive model to the ideal and random predictive models. It
lets you determine the percentage of the population to contact to reach a specific
percentage of the actual positive target.
Example
A company wants to do a mailing
campaign. They have built a predictive model to target to which customers to send
the campaign. The predictive model will classify the customers into two
categories:- Positive Targets: The customers will response to the campaign.
- Negative Targets: The customers will not response to the campaign.
The predictive model debrief displays the following Detected Target curve:
You can determine that
by selecting 30% of the total population:
- With a random predictive model, you would reach 30% of the positive population (= population that will response to the mailing).
- With a perfect predictive model, you would reach 100% of the positive population (= population that will response to the mailing).
- With the Smart Predict predictive model (the validation curve), you would reach 78% of the positive population (= population that will response to the mailing).