Decision Tree Classification (ML_CN_6)

This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.

This algorithm works by creating a tree-like model of decisions and their possible consequences. The model starts with a root node that represents the entire dataset and branches out into different nodes that represent possible decisions or features that can be used to split the data into smaller groups.

At each node, the algorithm chooses the feature that results in the greatest information gain, meaning the feature that provides the most information about the class labels of the data points. The process continues recursively until a leaf node is reached, which represents a final decision or classification for the data point.

Decision tree classification is a popular algorithm because it is easy to understand and interpret, and it can work well with both categorical and numerical data.

Sample Request

Build a Decision Tree Classification model named, "ClassicModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_CN_6",
    "args": {
        "model_name": "ClassicModel",
        "criterion": "gini",
        "random_state": 0
    }
}

Building a Decision Tree Classification model

Decision Tree 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.
criterion	String	function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain (defaults to gini)
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": "{\"ClassicModel\": {\"function_code\": \"ML_R_3\", \"model_url\": ""}}"
    }
}

Python

// Some code

Node

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

decisionTreeClassification = await client.decision_tree_classification(project_id, parent_id, block_id, {
    model_name: "ClassicModel",
    criterion: "gini",
    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_CN_6_P",
    "args": {
        "model_name": "ClassicModel",
        "test_data": ""
    }
}

Predicting with Decision Tree Classification

Decision Tree 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

decisionTreeClassificationPredict = await client.decision_tree_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_6_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

decisionTreeClassificationMetrics= await client.decision_tree_classification_metrics(project_id, parent_id, block_id, {
    model_name: "SimpleModel",

});