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."

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

Shap Explain

POST https://autogon.ai/api/v1/engine/start

Request Body

Name	Type	Description
project_id*	int	ID of the current project
parent_id*	int	ID of the previous block
block_id*	int	ID of the current block
function_code*	String	Function code for the current block
model_name*	String	Name of the pre-trained model to be used for analysis

200: OK StateManagement object

{
    "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}"
    }
}

Python

// Some code

Node

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",

});