Principal Component Analysis (DP_PCA)

This function reduces the dimensionality using PCA

Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space. The input data is centered but not scaled for each feature before applying the SVD

Sample Request

{
    "project_id": 41,
    "block_id": 10,
    "parent_id": 9,
    "function_code": "DP_PCA",
    "args": {
        "n_components": 3,
        "dataset": false,
        "xtrain": true,
        "xtest": true,
        "x": true,
        "ytrain": false,
        "ytest": false,
        "y": false
    }
}

Parameter Details

Principal Component Analysis

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

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

args*

object

block arguments

n_components

float

int, float or 'mle'

Number of components to keep. If n_components is not set, all components are kept Defaults to 'null'

dataset/x/y/xtrain/ytrain/xtest/ytest

bool

variables to apply function

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