> For the complete documentation index, see [llms.txt](https://docs.autogon.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.autogon.ai/autogon-engine-studio/machine-learning/random-forest-classification-ml_cn_7.md).

# Random Forest Classification (ML\_CN\_7)

This function combines multiple decision trees and aggregates their results to make predictions.

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 %}