Decision Tree Regression (ML_R_5)

This function splits the data into smaller subsets while at the same time an associated decision rule is used to predict the target variable, built in the form of a tree structure.

The final prediction is made by traversing the tree from the root to a leaf node, where the value of the target variable is stored. It is simple to understand and interpret, but can be prone to overfitting, particularly when the tree is deep and the data is noisy.

It is most suitable for continuous target variables and it's popularly used in many applications.

Sample Request

Build a multiple linear regression model named, "SimpleModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_R_5",
    "args": {
        "model_name": "SimpleModel",
        "random_state": 0
    }
}

Building a Decision Tree Regression model

Decision Tree 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).

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

decisionTreeRegression= await client.decision_tree_regression(project_id, parent_id, block_id, {
    model_name: "SimpleModel",
    random_state: 0
});

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_5_P",
    "args": {
        "model_name": "SimpleModel",
        "test_data": ""
    }
}

Predicting with Decision Tree Regression

Decision Tree 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

decisionTreeRegressionPredict = await client.decision_tree_regression_predict(project_id, parent_id, block_id, {
   model_name: "SimpleModel",
});