Max Fit Predictions

Post a maxfit prediction task to the CSA Prediction Engine

The maxfit endpoint focuses on maximizing the fit of a relevance-based partial sample regression by evaluating psr across a range of censors and censor thresholds. Whether you’re forecasting or analyzing outcomes, maxfit ensures that your results align closely with the underlying data patterns with confidence.

Quick Start

Getting started with the maxfit endpoint is simple. Supply the access ID along with prediction inputs such as dependent variable, independent variables, and prediction settings. The API processes the task to optimize fit, offering valuable insights with minimal setup.

post

Essential input arguments for invoking relevance-based maxfit predictions.

Authorizations

x-api-key

x-api-keystringRequired

API key required to access the CSA Prediction Engine API

Header parameters

Content-TypestringOptional

The content type of the request (application/json)

Example: application/json

ConnectionstringOptional

Connection header value (keep-alive)

Example: keep-alive

Body

application/json

access_idstringRequired

User access identification string for the CSA Prediction Engine

Example: CSA_ACCESS_ID

Responses

200

Response body of a successful post job (maxfit).

application/json

job_idintegerOptional

Prediction task job ID

Example: 1074

job_codestringOptional

Prediction task job code

Example: 66cfc21f7effa501

post

/maxfit

Python Example

import requests
import json

url = "https://api.csanalytics.io/v2/prediction-engine/maxfit"

payload = {
    "y": [[18.8], [2.3], [3.0], [6.7], [15.2], [20.9], [4.0]],
    "X": [[25.0,2.0,76.0,53.7,23.1],[29.0,1.0,78.0,39.1,5.7],[32.0,2.0,77.0,54.1,5.3],[30.0,5.0,82.0,68.7,8.7],[24.0,1.0,69.0,51.0,20.3],[25.0,2.0,79.0,50.2,20.0],[35.0,3.0,80.0,51.5,9.4]],
    "theta": [[24.0, 5.0, 81.0, 60.0, 11.0]]
}

headers = {
  'x-api-key': 'CSA_API_KEY',
  'Content-Type': 'application/json',
  'Connection': 'keep-alive'
}

response = requests.request("POST", url, headers=headers, data=json.dumps(payload))

200

Response body of a successful post job (maxfit).

{
  "job_id": 1074,
  "job_code": "66cfc21f7effa501"
}

Custom Settings

With the maxfit endpoint, you can fine-tune various settings to achieve optimal fit for your prediction tasks. Customize options like the threshold range and step-size resolution to optimize your results. This makes it ideal for users who need precision and control in their prediction task.

post

Extended specifications for invoking relevance-based maxfit predictions.

Authorizations

x-api-key

x-api-keystringRequired

API key required to access the CSA Prediction Engine API

Header parameters

Content-TypestringOptional

The content type of the request (application/json)

Example: application/json

ConnectionstringOptional

Connection header value (keep-alive)

Example: keep-alive

Body

application/json

access_idstringRequired

User access identification string for the CSA Prediction Engine

Example: CSA_ACCESS_ID

verify_missing_databooleanOptional

Affects how mean and covariance are computed. When true, uses only complete rows (observations with no missing values) for mean and covariance. When false, mean and covariance use nan-aware methods (column-wise nanmean, pairwise valid counts). In both modes, rows with missing values receive zero prediction weight because relevance cannot be computed for them.

Default: falseExample: false

is_threshold_percentbooleanOptional

Specify whether the thresholds specified is in percentage (decimal) units.

Default: trueExample: true

eval_typestring · enumOptional

Specify evaluation censor type: relevance, similarity, or both.

Default: bothExample: bothPossible values:

most_evalbooleanOptional

Specify the direction of the threshold evaluation on the corresponding censor type.

• true: [eval_type] score > threshold
• false: [eval_type] score < threshold

Default: trueExample: true

thresholdnumber · float[]Optional

Array of threshold values to evaluate the censor function (relevance or similarity). For maxfit and grid, users typically specify an array of thresholds. If not specified, the default is [0, 0.20, 0.50, 0.80].

Default: [0,0.2,0.5,0.8]Example: [0,0.2,0.5,0.8]

Responses

200

Response body of a successful post job (maxfit).

application/json

job_idintegerOptional

Prediction task job ID

Example: 1074

job_codestringOptional

Prediction task job code

Example: 66cfc21f7effa501

post

/maxfit

Python Example

import requests
import json

url = "https://api.csanalytics.io/v2/prediction-engine/maxfit"

payload = {
    "y": [[18.8], [2.3], [3.0], [6.7], [15.2], [20.9], [4.0]],
    "X": [[25.0, 2.0, 76.0, 53.7, 23.1], [29.0, 1.0, 78.0, 39.1, 5.7], [32.0, 2.0, 77.0, 54.1, 5.3], [30.0, 5.0, 82.0, 68.7, 8.7], [24.0, 1.0, 69.0, 51.0, 20.3], [25.0, 2.0, 79.0, 50.2, 20.0], [35.0, 3.0, 80.0, 51.5, 9.4]],
    "theta": [[24.0, 5.0, 81.0, 60.0, 11.0]],
    "threshold": [0, 0.20, 0.50, 0.80],
    "is_threshold_percent": true,
    "most_eval": true,
    "eval_type": "both"
}

headers = {
  'x-api-key': 'CSA_API_KEY',
  'Content-Type': 'application/json',
  'Connection': 'keep-alive'
}

response = requests.request("POST", url, headers=headers, data=json.dumps(payload))

print(response.text)

200

Response body of a successful post job (maxfit).

{
  "job_id": 1074,
  "job_code": "66cfc21f7effa501"
}