# XGBoost (MS\_XGBOOST)

XGBoost (Extreme Gradient Boosting) is a machine learning algorithm used for both classification and regression tasks. It is based on the gradient boosting technique, which iteratively trains weak models (usually decision trees) on the residuals of the previous models.

XGBoost optimizes a regularized objective function by minimizing the sum of the loss function and a penalty term that encourages simpler models and reduces overfitting. It also includes several advanced features, such as weighted quantile sketch for handling sparse data and cache-aware computing for faster training.

## Sample Request

Build an XGBoost model named, *"XGBoost"*

```javascript
{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "MS_XGBOOST",
    "args": {
        "model_name": "XGBoost"
    }
}
```

## Building a XGBoost model

## XGBoost

<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 %}

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

```
// Some code
```

{% endtab %}

{% tab title="Node" %}

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

xgboost = await client.xgboost(project_id, parent_id, block_id, {
    model_name: "XGBoost"
});
```

{% 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": "MS_XGBOOST_P",
    "args": {
        "model_name": "XGBoost",
        "test_data": ""
    }
}
```

## Predicting with XGBoost

## 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 %}

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

```
// Some code
```

{% endtab %}

{% tab title="Node" %}

<pre class="language-javascript"><code class="lang-javascript">const project_id = 1
const parent_id = 7
const block_id = 8

kmeansClustering = await client.xgboost_predict(project_id, parent_id, block_id, {
<strong>    model_name: "XGBoost",
</strong><strong>    test_data: ""
</strong>});
</code></pre>

{% endtab %}
{% endtabs %}

## Sample Request

Evaluate model metrics

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

## XGBoost 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 %}

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

```python
```

{% endtab %}

{% tab title="Node" %}

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

kmeansClusteringPredict = await client.xgboost_metrics(project_id, parent_id, block_id, {
    model_name: "SimpleModel",

});
```

{% endtab %}
{% endtabs %}