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On this page
  • Sample Request
  • Building a Decision Tree Regression model
  • Decision Tree Regression
  • Sample Request
  • Predicting with Decision Tree Regression
  • Decision Tree Regression Predict

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  1. Autogon Engine (Studio)
  2. Machine Learning

Decision Tree Regression (ML_R_5)

This function splits the data into smaller subsets while at the same time an associated decision rule is used to predict the target variable, built in the form of a tree structure.

The final prediction is made by traversing the tree from the root to a leaf node, where the value of the target variable is stored. It is simple to understand and interpret, but can be prone to overfitting, particularly when the tree is deep and the data is noisy.

It is most suitable for continuous target variables and it's popularly used in many applications.

Sample Request

Build a multiple linear regression model named, "SimpleModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_R_5",
    "args": {
        "model_name": "SimpleModel",
        "random_state": 0
    }
}

Building a Decision Tree Regression model

Decision Tree Regression

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

Controls the randomness of the estimator (defaults to 0).

{
    "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": "{\"SimpleModel\": {\"function_code\": \"ML_R_3\", \"model_url\": ""}}"
    }
}
// Some code
const project_id = 1
const parent_id = 7
const block_id = 8

decisionTreeRegression= await client.decision_tree_regression(project_id, parent_id, block_id, {
    model_name: "SimpleModel",
    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_R_5_P",
    "args": {
        "model_name": "SimpleModel",
        "test_data": ""
    }
}

Predicting with Decision Tree Regression

Decision Tree Regression 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

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

decisionTreeRegressionPredict = await client.decision_tree_regression_predict(project_id, parent_id, block_id, {
   model_name: "SimpleModel",
});
PreviousSupport Vector Regression (ML_R_4)NextRandom Forest Regression (ML_R_6)

Last updated 2 years ago

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