Random Forest Classification (ML_CN_7)

This function combines multiple decision trees and aggregates their results to make predictions.

Random Forest is an ensemble machine learning algorithm that combines multiple decision trees to improve performance and reduce overfitting. It creates a set of decision trees by randomly selecting subsets of the features and data samples, and then aggregates the results of the trees to make predictions.

Sample Request

Build a Random Forest Classification model named, "ClassicModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_CN_7",
    "args": {
        "model_name": "ClassicModel",
        "criterion": "gini"
    }
}

Building a Random Forest Classification model

Random Forest Classification

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	Seed for random number generation. If provided, it ensures reproducibility of the random processes in the algorithm. If not provided, a random seed will be used
n_estimators	int	The number of trees in the forest (ensemble) used by the algorithm. Each tree contributes to the final prediction. Larger values generally improve performance, but also increase computation time.
criterion	String	The function to measure the quality of a split in the decision tree. Common criteria include gini for the Gini impurity and entropy for information gain. The choice of criterion affects how the decision tree grows and splits its nodes.

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

Second Tab

// 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": "ML_CN_7_P",
    "args": {
        "model_name": "ClassicModel",
        "test_data": ""
    }
}

Predicting with Random Forest Classification

Random Forest Classification 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

randomForestClassificationPredict = await client.random_forest_classification_predict(project_id, parent_id, block_id, {
    model_name: "ClassicModel",
    test_data: ""
});

Sample Request

Evaluate model metrics

{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "ML_CN_7_M",
    "args": {
        "model_name": "ClassicModel"
    }
}

Decision Tree Classification 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

// Some code

Node

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

randomForestClassificationMetrics= await client.random_forest_classification_metrics(project_id, parent_id, block_id, {
    model_name: "SimpleModel",

});