# Random Forest Regression (ML\_R\_6)

The algorithm randomly selects a subset of features and samples to train each decision tree, making it less prone to overfitting compared to a single decision tree. The final prediction is made by averaging the predictions of all the trees in the forest. It can be used for both continuous and categorical target variables and is often considered to be a robust and accurate algorithm for regression tasks.

## Sample Request

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

```javascript
{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_R_6",
    "args": {
        "model_name": "SimpleModel",
        "random_state": 0,
        "n_estimators": 100
    }
}
```

## Building a Random Forest Regression model

## Random Forest 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.              |
| random\_state                                    | int    | Controls the randomness of the estimator (defaults to 0). |
| n\_estimators                                    | int    | The number of trees in the forest (defaults to 100).      |

{% 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

randomForestRegression= await client.random_forest_regression(project_id, parent_id, block_id, {
    model_name: "SimpleModel",
    random_state: 0,
    n_estimators: 100
});
```

{% 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_6_P",
    "args": {
        "model_name": "SimpleModel",
        "test_data": ""
    }
}
```

## Predicting with Random Forest Regression

## Random Forest 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

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

{% endtab %}
{% endtabs %}