Simple Linear Regression (ML_R_1)

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

It uses a linear technique to predict the value of the dependent variable based on the value of the independent variable.

The values of the coefficients of the equation are found by minimizing the difference between the observed values of the dependent variable and the predicted values by the equation.

It can help understand the relationship between two variables and make

Sample Request

Build a simple linear regression model named, "SimpleModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_R_1",
    "args": {
        "model_name": "SimpleModel"
    }
}

Building a Simple Linear Regression model

Simple 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\": ""}}"
    }
}

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_1_P",
    "args": {
        "model_name": "SimpleModel",
        "test_data": ""
    }
}

Predicting with Simple Linear Regression

Simple 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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