Power Transformations (Box-Cox, Yeo-Johnson)

Applies power transformations to make data more Gaussian-like. Box-Cox for positive data, Yeo-Johnson for any data. Improves linear model assumptions and reduces skewness. [Tier: ENTERPRISE, Credits: 10]

POST /quantlib/ml/preprocessing/transform

Power Transformations (Box-Cox, Yeo-Johnson)

cURL

curl --request POST \
  --url https://api.fincept.in/quantlib/ml/preprocessing/transform \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "X": [
    [
      100,
      0.5
    ],
    [
      200,
      1.3
    ],
    [
      150,
      0.9
    ]
  ],
  "method": "box_cox"
}
'

200

{
  "success": true,
  "data": {
    "transformed": [
      [
        4.605,
        -0.693
      ],
      [
        5.298,
        0.262
      ],
      [
        5.011,
        -0.105
      ]
    ],
    "method": "box_cox"
  }
}

Authorizations

X-API-Key · string · header · required

API key for authentication. Get your key at https://api.fincept.in/auth/register

Body

application/json

X · number[][] · required

Feature matrix

Example:

[[100, 0.5], [200, 1.3], [150, 0.9]]

method · enum<string> · default: power

Transformation method

Available options: power, box_cox

Example:

"box_cox"

Response

200

application/json

Transformed data

success · boolean

Example:

true

data · object

Show child attributes