Multiple Linear Regression (ML_R_2)

This function models the relationship between more independent variables. The objective is to predict the value of an output variable based on the value of input variables.

The method uses a linear technique to describe the relationship between the variables, and the coefficients of the equation are determined by minimizing the sum of the squared differences between the observed values of the dependent variable and the values predicted by the equation.

It's a way to analyze multiple factors and understand how they influence a certain outcome.

Sample Request

Build a multiple linear regression model named, "SimpleModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_R_2",
    "args": {
        "model_name": "SimpleModel"
    }
}

Building a Multiple Linear Regression model

Multiple Linear Regression

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

Request Body

{
    "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": "{\"SimpleModel\": {\"function_code\": \"ML_R_1\", \"model_url\": ""}}"
    }
}
// Some code

Sample Request

Make predictions with the pre-built model passing an optional test data.

{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "ML_R_2_P",
    "args": {
        "model_name": "SimpleModel",
        "test_data": ""
    }
}

Predicting with Multiple Linear Regression

Multiple Linear Regression Predict

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

Request Body

{
    "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\": ""}"
    }
}
// Some code

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