# Shap Explain (ML\_SHAP)

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 %}