> 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/machine-learning/polynomial-linear-regression-ml_r_3.md).

# Polynomial Linear Regression (ML\_R\_3)

It does this by fitting a polynomial equation to the data, rather than a straight line. The degree of the polynomial equation can be adjusted to improve the fit of the model to the data. 

This approach can be useful when the relationship between the variables is more complex than a simple linear relationship.

## Sample Request

Build a multiple linear regression model named, *"PolyModel"*

```javascript
{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_R_3",
    "args": {
        "model_name": "SimpleModel",
        "degree": 2
    }
}
```

## Building a Polynomial Linear Regression model

## Polynomial Linear Regression

<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.          |
| degree                                           | int    | maximum degree of polynomial features (defaults to 2) |

{% 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": "{\"SimpleModel\": {\"function_code\": \"ML_R_3\", \"model_url\": ""}}"
    }
}
```

{% endtab %}
{% endtabs %}

{% tabs %}
{% tab title="Python" %}

```
// Some code
```

{% endtab %}

{% tab title="Node" %}

```javascript
const project_id = 1
const parent_id = 7
const block_id = 8

polynomialLinearRegression = await client.polynomial_linear_regression(project_id, parent_id, block_id, {
    model_name: "SimpleModel",
    degree: 2
});
```

{% 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": "ML_R_3_P",
    "args": {
        "model_name": "SimpleModel",
        "test_data": ""
    }
}
```

## Predicting with Polynomial Linear Regression

## Multiple Linear Regression 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 %}

{% tabs %}
{% tab title="Python" %}

```
// Some code
```

{% endtab %}

{% tab title="Node" %}

```javascript
const project_id = 1
const parent_id = 7
const block_id = 8

polynomialLinearRegressionPredict = await client.polynomial_linear_regression_predict(project_id, parent_id, block_id, {
       model_name: "SimpleModel",
      test_data: ""
    });
```

{% endtab %}
{% endtabs %}


---

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