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  1. Autogon Engine (Studio)
  2. Machine Learning

Grid Search (ML_GRID)

This function exhaustively searches for the optimal combination of hyperparameter values for a machine learning model.

Grid search is a hyperparameter tuning technique used to find the optimal combination of hyperparameter values for a machine learning model. It works by systematically searching through a predefined grid of hyperparameter values, evaluating the model's performance using cross-validation at each point in the grid. Grid search helps to identify the hyperparameter configuration that yields the best performance, enhancing the model's accuracy and generalization ability.

Sample Request

This request is performing a grid search for hyperparameter tuning on the "RandomForest" model. It searches for the best combination of hyperparameters "n_estimators", "random_state", and "criterion" by evaluating the model's performance with different values provided in the "param_grid."

{
    "project_id": 13,
    "parent_id": 3,
    "block_id": 4,
    "function_code": "ML_GRID",
    "args": {
        "model_name": "RandomForest",
        "param_grid": [
            {
                "n_estimators": [
                    5,
                    10,
                    60,
                    100
                ]
            },
            {
                "random_state": [
                    0,
                    42,
                    60
                ],
                "criterion": [
                    "gini",
                    "entropy"
                ]
            }
        ]
    }
}

Grid Search

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 analysis

param_grid

object

set of hyperparameter values that the grid search will exhaustively explore to find the optimal combination of hyperparameters

{
    "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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