Overview of the prediction model

AI Builder prediction models analyze patterns in your data, and learns to associate historical patterns with outcomes. Based on past results, the prediction model learns patterns, and detects them in new data to predict future outcomes.

Use the prediction AI model to explore business questions that can answered as one of two available options (binary), multiple possible outcomes, or where the answer is a number.

Binary prediction

Binary prediction is when the question asked has two possible answers - yes/no, tru/false, on-time/late, go/no-go, etc. Examples of question that use binary prediction include:

  • Is an applicant eligible for membership?
  • Is this transaction likely to be fraudulent?
  • Is a customer a good candidate for a marketing campaign?
  • is an account likely to pay their invoices on time?

Multiple outcome prediction

Multiple outcome prediction is when the question can be answered from a list of more than 2 possible outcomes. Examples of multiple outcome prediction include:

  • Will a shipment arrive early, on-time, late, or very late?
  • Which product would a customer be interested in?

Numerical prediction

Numerical prediction is when the question is answered with a number. Examples of multiple outcome prediction include:

  • How many days for a shipment to arrive?
  • How many calls should an agent handle in a day?
  • How many items do we need to keep in inventory?
  • How many leads should a sales team be able to convert in a month?

See also

Feature availability by region
Prediction model prerequisites