# Overview of the prediction model

AI Builder prediction models analyze patterns in historical data that you provide. Prediction models learn to associate those patterns with outcomes. Then, we use the power of AI to detect learned patterns in new data, and use them to predict future outcomes.

Use the prediction model to explore business questions that can be answered as one the following ways:

- From two available options (binary).
- From multiple possible outcomes.
- Where the answer is a number.

## Binary prediction

Binary prediction is when the question asked has two possible answers. For example: yes/no, true/false, on-time/late, go/no-go, and so on. Examples of questions 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 two 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 numerical 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 convert in a month?

### See also

Feature availability by region

Prediction model prerequisites

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