# Kernel SVM (ML\_CN\_4)

Kernel SVM works by finding a line or a hyperplane that separates the data into different classes. However, in cases where the data is not linearly separable, a kernel function is used to transform the data into a higher-dimensional space where it becomes separable.

Kernel SVM is a powerful algorithm that can handle complex and nonlinear data. It is widely used in image recognition, natural language processing, and other fields where the data is not easily separable in a linear fashion.

## Sample Request

Build a Kernel SVM model named, *"ClassicModel"*

```javascript
{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_CN_4",
    "args": {
        "model_name": "ClassicModel",
        "kernel": "rbf",
        "random_state": 0
    }
}
```

## Building a Kernel SVM model

## Kernel SVM 

<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.                               |
| kernel                                           | String | Specifies the kernel type to be used in the algorithm (defaults to "rbf"). |
| random\_state                                    | int    | Controls the randomness of the estimator (defaults to 0).                  |

{% 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" %}

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

kernelSvm = await client.kernel_svm(project_id, parent_id, block_id, {
<strong>    model_name: "ClassicModel",
</strong>    kernel: "rbf",
<strong>    random_state: 0
</strong>});
</code></pre>

{% 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_CN_4_P",
    "args": {
        "model_name": "ClassicModel",
        "test_data": ""
    }
}
```

## Predicting with Kernel SVM

## Kernel SVM 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" %}

```
```

{% endtab %}

{% tab title="Node" %}

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

kernelSvmPredict = await client.kernel_svm_predict(project_id, parent_id, block_id, {
    model_name: "ClassicModel",
<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": "ML_CN_4_M",
    "args": {
        "model_name": "ClassicModel"
    }
}
```

## Kernel SVM  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" %}

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


kernelSvmMetrics= await client.kernel_svm_metrics(project_id, parent_id, block_id, {
    model_name: "SimpleModel",

});
```

{% endtab %}

{% tab title="Node" %}

```
```

{% endtab %}
{% endtabs %}