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On this page
  • Sample Request
  • Building a Logistic Regression model
  • Logistic Regression
  • Sample Request
  • Predicting with Logistic Regression
  • Logistic Regression Predict
  • Sample Request
  • Logistic Regression Metrics

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  1. Autogon Engine (Studio)
  2. Machine Learning

Logistic Regression (ML_CN_1)

This function performs analysis on a dataset and returns the predicted binary outcome based on the input independent variables.

This function analyzes a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes).

It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables. The model is based on the relationship between the independent variables and the probability of the binary outcome.

Logistic regression models the probability that an event belongs to a certain category, then makes predictions based on the maximum likelihood of the observed data.

Sample Request

Build a logistic regression model named, "ClassicModel"

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

Building a Logistic Regression model

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

LogisticRegression = await client.logistic_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_CN_1_P ",
    "args": {
        "model_name": "ClassicModel",
        "test_data": null
    }
}

Predicting with Logistic Regression

Logistic 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 Client = require("./src/client");

const client = new Client(process.env.AUTOGON_API_KEY);

projectId = 1;
parentId = 8;
blockId = 9;


logisticRegression = (await client.logistic_regression(projectId, parentId, blockId, {
    model_name: "LogisticModel",
    test_data: null
})).data

Sample Request

Evaluate model metrics

{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "ML_CN_1_P ",
    "args": {
        "model_name": "ClassicModel"
    }
}

Logistic Regression 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

{
    "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}"
    }
}
// Some code
const project_id = 1
const parent_id = 7
const block_id = 8
    
LogisticRegressionMetrics= await client.logistic_regression_metrics(project_id, parent_id, block_id, {
    model_name: "SimpleModel",

});
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Last updated 1 year ago

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