# Grid Search (ML\_GRID)

Grid search is a hyperparameter tuning technique used to find the optimal combination of hyperparameter values for a machine learning model. It works by systematically searching through a predefined grid of hyperparameter values, evaluating the model's performance using cross-validation at each point in the grid. Grid search helps to identify the hyperparameter configuration that yields the best performance, enhancing the model's accuracy and generalization ability.

## Sample Request

This request is performing a grid search for hyperparameter tuning on the "RandomForest" model. It searches for the best combination of hyperparameters `"n_estimators"`, `"random_state"`, and `"criterion"` by evaluating the model's performance with different values provided in the "param\_grid."

```javascript
{
    "project_id": 13,
    "parent_id": 3,
    "block_id": 4,
    "function_code": "ML_GRID",
    "args": {
        "model_name": "RandomForest",
        "param_grid": [
            {
                "n_estimators": [
                    5,
                    10,
                    60,
                    100
                ]
            },
            {
                "random_state": [
                    0,
                    42,
                    60
                ],
                "criterion": [
                    "gini",
                    "entropy"
                ]
            }
        ]
    }
}
```

## Grid Search

<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 analysis                                                                          |
| param\_grid                                      | object | set of hyperparameter values that the grid search will exhaustively explore to find the optimal combination of hyperparameters |

{% 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
    
LogisticRegressionMetrics= await client.logistic_regression_metrics(project_id, parent_id, block_id, {
    model_name: "SimpleModel",

});
```

{% endtab %}
{% endtabs %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.autogon.ai/autogon-engine-studio/machine-learning/grid-search-ml_grid.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.