> ## Documentation Index
> Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Task Types

Predictive queries can address a variety of predictive problems. The **task type** is determined by the structure of your query and influences:

* The commands and operators used in the query.

* The available evaluation metrics.

* Explainable AI (XAI) options.

Kumo **automatically determines the task type** based on your Predictive Query (PQL).

## Common Task Types

| Task Type                              | Output                 | PQL Example                                                                                         |
| -------------------------------------- | ---------------------- | --------------------------------------------------------------------------------------------------- |
| Regression                             | Continuous real number | `PREDICT customers.age FOR EACH customers.customer_id`                                              |
| Binary Classification                  | True or False          | `PREDICT fraud_reports.is_fraud FOR EACH transactions.id WHERE transactions.type = "bank transfer"` |
| Multiclass + Multilabel Classification | Class label            | `PREDICT FIRST(purchases.type, 0, 7) FOR EACH users.user_id`                                        |
| Link Prediction                        | List of items          | `PREDICT LIST_DISTINCT(transactions.article_id, 0, 7) RANK TOP 10 FOR EACH customers.customer_id`   |

## Examples

### Regression

A **regression task** predicts a continuous value. For example, predicting the **total amount of purchases per customer over the next 30 days**:

```Text PQL theme={null}
PREDICT SUM(TRANSACTIONS.PRICE, 0, 30, days)
FOR EACH CUSTOMERS.CUSTOMER_ID
```

Kumo recognizes this as a **regression task** since it uses the `SUM()` operator without a boolean condition.

### Binary Classification

A **binary classification task** predicts a **true/false outcome**. For example, predicting which customers will make no transactions in the next 30 days:

```Text PQL theme={null}
PREDICT COUNT(TRANSACTIONS.*, 0, 30, days) = 0
FOR EACH CUSTOMERS.CUSTOMER_ID
```

* The `=` operator makes this a **classification task**.

* Changing it to `>` would modify the **positive label** of the prediction.

For more Predictive Query examples across different task types, see the [Predictive Query Examples](/examples/predictive-query).