Support Vector Regression (ML_R_4)

This function can be used for solving both linear and non-linear problems.

It is based on the concept of support vectors, which are the data points that are closest to the decision boundary, or the line that separates the data into different classes. SVR algorithm aims to find the best line that maximizes the margin between the support vectors and the decision boundary, this line is known as the support vector.

This function aims to find the best line that maximizes the margin between the support vectors and the decision boundary, this line is known as the support vector. It can also be used with kernel functions to find the best decision boundary in non-linear regression problems.

It is a powerful algorithm that is able to handle high dimensional and non-linear data, making it suitable for various regression problems.

Sample Request

Build a multiple linear regression model named, "SimpleModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_R_4",
    "args": {
        "model_name": "SimpleModel",
        "kernel": "rbf"
    }
}

Building a Support Vector Regression model

Support Vector Regression

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

Request Body

Name	Type	Description
project_id*	int	The id of the current project
block_id*	int	The id of the current block
function_code*	string	The function code for current block
parent_id*	int	The id of the previous block
args*	object	Block arguments
model_name*	String	Name of the model to be used for prediction.
kernel	String	Specifies the kernel type to be used in the algorithm (defaults to "rbf").

200 Statemanagement object

{
    "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_3\", \"model_url\": ""}}"
    }
}

Python

// Some code

Node

const project_id = 1
const parent_id = 7
const block_id = 8

supportVectorRegression = await client.support_vector_regression(project_id, parent_id, block_id, {
    model_name: "SimpleModel",
    kernel: "rbf"
});

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_4_P",
    "args": {
        "model_name": "SimpleModel",
        "test_data": ""
    }
}

Predicting with Support Vector Regression

Support Vector Regression Predict

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

Request Body

Name	Type	Description
model_name*	String	Name of previously trained model to be used for prediction
test_data	String	Input data for prediction. Defaults to x_train_url in StateManagment
project_id*	int	ID of the current project
block_id*	int	ID of the current block
parent_id*	int	ID of the previous block
function_code*	String	Function code for the current block

200: OK Statemanagement object

{
    "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\": ""}"
    }
}

Python

// Some code

Node

const project_id = 1
const parent_id = 7
const block_id = 8

supportVectorRegressionPredict = await client.support_vector_machine_predict(project_id, parent_id, block_id, {
       model_name: "SimpleModel",
      test_data: "http://cloud.autogonai.s3.amazonaws.com/f6fc6cf1-bdba-48d0-a7ac-fddd9609c826.csv"
    });