> 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/shap-explain-ml_shap.md).

# Shap Explain (ML\_SHAP)

This function provides interpretable insights into machine learning model predictions by explaining the contribution of each feature to the output.

Shap (SHapley Additive exPlanations) is an Explainable AI (XAI) method that provides interpretable insights into the predictions made by a logistic regression model. It allows us to understand the contribution of each independent variable in determining the probability of a binary outcome (e.g., Yes/No, True/False). 

Shap values help to uncover the impact of individual features on the model's predictions, enhancing transparency and facilitating model evaluation and decision-making.

## Sample Request

Perform model analysis using SHAP (SHapley Additive exPlanations) on a specific model named "RandomForest."

```javascript
{
    "project_id": 13,
    "parent_id": 3,
    "block_id": 4,
    "function_code": "ML_SHAP",
    "args": {
        "model_name": "RandomForest"
    }
}
```

## Shap Explain

<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 |

{% 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 %}