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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.

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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
    }
}

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Building a Decision Tree Classification model

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Decision Tree Classification

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

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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).

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Sample Request

Make predictions with the pre-built model passing an optional test data.

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Predicting with Decision Tree Classification

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Decision Tree Classification Predict

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

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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

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Sample Request

Evaluate model metrics

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Decision Tree Classification Metrics

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

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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

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