Feature Sampling (DP_FSP)

This functionality samples a dataset into X and Y features

When you have a dataset with many features, it can be difficult to determine which features are most relevant for making predictions. Feature sampling is a technique that involves randomly selecting a subset of features from the dataset to use in a machine learning model.

To perform feature sampling, you would typically split the dataset into two parts: the X features and the Y features. The X features are the input features, which are used to make predictions, while the Y features are the output features, which are the values that you are trying to predict.

Sample Request

This request splits the dataset into X and Y values based on the specified boundaries

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "DP_FSP",
    "args": {
        "x_boundaries": ":, :-1",
        "y_boundaries": ":, -1"
    }   
}

Feature Sampling

Sample data into X and Y

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

Split the dataset into X and Y values

Request Body

Name	Type	Description
project_id*	int	current project ID
parent_id*	int	parent block ID
block_id*	int	current block ID
function_code*	String	block's function code
x_boundaries*	String	slicing boundaries for x features
args	object	block arguments
y_boundaries*	String	slicing boundaries for y features

200: OK Data Encode Successful

{
    "status": "true",
    "message": {
        "id": 3,
        "project": 1,
        "block_id": 7,
        "parent_id": 6,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": ""
    }
}

Python

// Some code

Node

projectId = 1
parentId = 6
blockId = 7

client.array_reshaping(projectId, parentId, blockId, {