> For the complete documentation index, see [llms.txt](https://docs.autogon.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.autogon.ai/autogon-engine-studio/automated-machine-learning/autoregression-auto_r_1.md).

# AutoRegression (AUTO\_R\_1)

This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.

In other words, it assigns a label to a new data point based on how similar it is to the existing data points, where similarity is defined by distance metric such as Euclidean or Manhattan. 

This function can be used for both supervised and unsupervised learning.

## Sample Request

Build an AutoRegression model named, *"*AutoRegression*"*

```javascript
{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "AUTO_R_1",
    "args": {
        "model_name": "AutoRegression",
        "time_left": 60,
        "run_time_limit": 30,
        "n_jobs": 1
    }
}
```

## Building a AutoRegression model

## AutoRegression

<mark style="color:green;">`POST`</mark> `https://autogon.ai/api/v1/engine/start`

#### Request Body

| Name                                             | Type   | Description                                  |
| ------------------------------------------------ | ------ | -------------------------------------------- |
| project\_id<mark style="color:red;">\*</mark>    | int    | The `id` of the current project              |
| block\_id<mark style="color:red;">\*</mark>      | int    | The `id` of the current block                |
| function\_code<mark style="color:red;">\*</mark> | string | The function code for current block          |
| parent\_id<mark style="color:red;">\*</mark>     | int    | The `id` of the previous block               |
| args<mark style="color:red;">\*</mark>           | object | Block arguments                              |
| model\_name<mark style="color:red;">\*</mark>    | String | Name of the model to be used for prediction. |

{% tabs %}
{% tab title="200 Statemanagement object" %}

```javascript
{
    "status": "true",
    "message": {
        "id": 8,
        "project": 1,
        "block_id": 8,
        "parent_id": 7,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": "{\"ClassicModel\": {\"function_code\": \"ML_R_3\", \"model_url\": ""}}"
    }
}
```

{% endtab %}
{% endtabs %}

## Sample Request

Make predictions with the pre-built model passing an optional test data.

```javascript
{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "AUTO_R_1_P",
    "args": {
        "model_name": "AutoRegressionAutoRegression"
    }
}
```

## Predicting with AutoRegression

## XGBoost Predict

<mark style="color:green;">`POST`</mark> `https://autogon.ai/api/v1/engine/start`

#### Request Body

| Name                                             | Type   | Description                                                            |
| ------------------------------------------------ | ------ | ---------------------------------------------------------------------- |
| model\_name<mark style="color:red;">\*</mark>    | String | Name of previously trained model to be used for prediction             |
| test\_data                                       | String | Input data for prediction. Defaults to `x_train_url` in StateManagment |
| project\_id<mark style="color:red;">\*</mark>    | int    | ID of the current project                                              |
| block\_id<mark style="color:red;">\*</mark>      | int    | ID of the current block                                                |
| parent\_id<mark style="color:red;">\*</mark>     | int    | ID of the previous block                                               |
| function\_code<mark style="color:red;">\*</mark> | String | Function code for the current block                                    |

{% tabs %}
{% tab title="200: OK Statemanagement object" %}

```javascript
{
    "status": "true",
    "message": {
        "id": 9,
        "project": 1,
        "block_id": 9,
        "parent_id": 8,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": "{\"y_pred_url\": ""}"
    }
}
```

{% endtab %}
{% endtabs %}

## Sample Request

Evaluate model metrics

```javascript
{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "AUTO_R_1_M",
    "args": {
        "model_name": "AutoRegression",
        "metric": "mae"
    }
}
```

## AutoRegression Metrics

<mark style="color:green;">`POST`</mark> `https://autogon.ai/api/v1/engine/start`

#### Request Body

| Name                                             | Type   | Description                                             |
| ------------------------------------------------ | ------ | ------------------------------------------------------- |
| project\_id<mark style="color:red;">\*</mark>    | int    | ID of the current project                               |
| parent\_id<mark style="color:red;">\*</mark>     | int    | ID of the previous block                                |
| block\_id<mark style="color:red;">\*</mark>      | int    | ID of the current block                                 |
| function\_code<mark style="color:red;">\*</mark> | String | Function code for the current block                     |
| model\_name<mark style="color:red;">\*</mark>    | String | Name of the pre-trained model to be used for evaluation |

{% tabs %}
{% tab title="200: OK StateManagement object" %}

```javascript
{
    "status": "true",
    "message": {
        "id": 1,
        "project": 12,
        "block_id": 10,
        "parent_id": 11,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": "{'confusion_matrix': '', 'accuracy': 0.9}"
    }
}
```

{% endtab %}
{% endtabs %}