Random Forest Regression (ML_R_6)

This function builds multiple decision trees and combines their outputs to make a final prediction.

The algorithm randomly selects a subset of features and samples to train each decision tree, making it less prone to overfitting compared to a single decision tree. The final prediction is made by averaging the predictions of all the trees in the forest. It can be used for both continuous and categorical target variables and is often considered to be a robust and accurate algorithm for regression tasks.

Sample Request

Build a multiple linear regression model named, "SimpleModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_R_6",
    "args": {
        "model_name": "SimpleModel",
        "random_state": 0,
        "n_estimators": 100
    }
}

Building a Random Forest Regression model

Random Forest 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.
random_state	int	Controls the randomness of the estimator (defaults to 0).
n_estimators	int	The number of trees in the forest (defaults to 100).

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

randomForestRegression= await client.random_forest_regression(project_id, parent_id, block_id, {
    model_name: "SimpleModel",
    random_state: 0,
    n_estimators: 100
});

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_6_P",
    "args": {
        "model_name": "SimpleModel",
        "test_data": ""
    }
}

Predicting with Random Forest Regression

Random Forest 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

randomForestRegressionPredict = await client.random_forest_regression_predict(project_id, parent_id, block_id, {
    model_name: "SimpleModel",
    test_data: ""
});