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

NameTypeDescription

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.

{
    "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\": ""}}"
    }
}
// Some code

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

NameTypeDescription

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

{
    "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

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

NameTypeDescription

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

{
    "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}"
    }
}

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