XGBoost (MS_XGBOOST)

This function is based on gradient boosting that iteratively trains weak models while optimizing a regularized objective function to reduce overfitting.

XGBoost (Extreme Gradient Boosting) is a machine learning algorithm used for both classification and regression tasks. It is based on the gradient boosting technique, which iteratively trains weak models (usually decision trees) on the residuals of the previous models.

XGBoost optimizes a regularized objective function by minimizing the sum of the loss function and a penalty term that encourages simpler models and reduces overfitting. It also includes several advanced features, such as weighted quantile sketch for handling sparse data and cache-aware computing for faster training.

Sample Request

Build an XGBoost model named, "XGBoost"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "MS_XGBOOST",
    "args": {
        "model_name": "XGBoost"
    }
}

Building a XGBoost model

XGBoost

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.

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": "{\"ClassicModel\": {\"function_code\": \"ML_R_3\", \"model_url\": ""}}"
    }
}

Python

// Some code

Node

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

xgboost = await client.xgboost(project_id, parent_id, block_id, {
    model_name: "XGBoost"
});

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": "MS_XGBOOST_P",
    "args": {
        "model_name": "XGBoost",
        "test_data": ""
    }
}

Predicting with XGBoost

XGBoost 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

kmeansClustering = await client.xgboost_predict(project_id, parent_id, block_id, {
    model_name: "XGBoost",
    test_data: ""
});

Sample Request

Evaluate model metrics

{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "MS_XGBOOST_P",
    "args": {
        "model_name": "ClassicModel"
    }
}

XGBoost Metrics

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

Request Body

Name	Type	Description
project_id*	int	ID of the current project
parent_id*	int	ID of the previous block
block_id*	int	ID of the current block
function_code*	String	Function code for the current block
model_name*	String	Name of the pre-trained model to be used for evaluation

200: OK StateManagement object

{
    "status": "true",
    "message": {
        "id": 1,
        "project": 12,
        "block_id": 10,
        "parent_id": 11,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": "{'confusion_matrix': '', 'accuracy': 0.9}"
    }
}

Python

Node

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

kmeansClusteringPredict = await client.xgboost_metrics(project_id, parent_id, block_id, {
    model_name: "SimpleModel",

});