# K-Nearest Neighbors - KNN (ML\_CN\_2)

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 a KNN model named, *"ClassicModel"*

```javascript
{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_CN_2",
    "args": {
        "model_name": "ClassicModel",
        "random_state": 0,
        "n_neighbors": 5,
        "distance": "minkowski",
        "p": 2
    }
}
```

## Building a K-Nearest Neighbors model

## K-Nearest Neighbors

<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\_neighbors                                     | int    | Number of neighbors to use by default for `kneighbors` queries (defaults to 5).                                                                               |
| distance                                         | String | Metric to use for distance computation. Default `minkowski`, which results in the standard Euclidean distance when `p = 2`.                                   |
| p                                                | int    | Power parameter for the Minkowski metric. When `p = 1,` this is equivalent to using `manhattan_distance`, and `euclidean_distance` for p = 2 (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": "{\"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

kNearestNeighbors = await client.k_nearest_neighbors(project_id, parent_id, block_id, {
    model_name: "ClassicModel",
    random_state: 0,
    n_neighbors: 5,
    distance: "minkowski",
    p: 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_CN_2_P",
    "args": {
        "model_name": "ClassicModel",
        "test_data": ""
    }
}
```

## Predicting with K-Nearest Neighbors

## K-Nearest Neighbors 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

kNearestNeighborsnPredict = await client.k_nearest_neighbors_predict(project_id, parent_id, block_id, {
   model_name: "ClassicModel",
    test_data: ""
});
```

{% endtab %}
{% endtabs %}

## Sample Request

Evaluate model metrics

```javascript
{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "ML_CN_2_M",
    "args": {
        "model_name": "ClassicModel"
    }
}
```

## K-Nearest Neighbors 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" %}

```
// Some code
```

{% endtab %}

{% tab title="Node" %}

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

kNearestNeighborsMetrics= await client.k_nearest_neighbors_metrics(project_id, parent_id, block_id, {
    model_name: "SimpleModel",
 
});
```

{% endtab %}
{% endtabs %}


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