# Random Forest Classification (ML\_CN\_7)

Random Forest is an ensemble machine learning algorithm that combines multiple decision trees to improve performance and reduce overfitting. It creates a set of decision trees by randomly selecting subsets of the features and data samples, and then aggregates the results of the trees to make predictions.

## Sample Request

Build a Random Forest Classification model named, *"ClassicModel"*

```javascript
{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_CN_7",
    "args": {
        "model_name": "ClassicModel",
        "criterion": "gini"
    }
}
```

## Building a Random Forest Classification model

## Random Forest Classification 

<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    | Seed for random number generation. If provided, it ensures reproducibility of the random processes in the algorithm. If not provided, a random seed will be used                                                                                |
| n\_estimators                                    | int    | The number of trees in the forest (ensemble) used by the algorithm. Each tree contributes to the final prediction. Larger values generally improve performance, but also increase computation time.                                             |
| criterion                                        | String | The function to measure the quality of a split in the decision tree. Common criteria include `gini` for the Gini impurity and `entropy` for information gain. The choice of criterion affects how the decision tree grows and splits its nodes. |

{% 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="Second Tab" %}

```
// Some code
```

{% 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_7_P",
    "args": {
        "model_name": "ClassicModel",
        "test_data": ""
    }
}
```

## Predicting with Random Forest Classification

## Random Forest Classification   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

randomForestClassificationPredict = await client.random_forest_classification_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_7_M",
    "args": {
        "model_name": "ClassicModel"
    }
}
```

## Decision Tree Classification   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

randomForestClassificationMetrics= await client.random_forest_classification_metrics(project_id, parent_id, block_id, {
    model_name: "SimpleModel",

});
```

{% endtab %}
{% endtabs %}


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