> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fincept.in/llms.txt
> Use this file to discover all available pages before exploring further.

# Classification Evaluation Metrics

> Calculates classification metrics: accuracy, precision, recall, and F1-score. Essential for evaluating binary classification models like credit default prediction. [Tier: ENTERPRISE, Credits: 10]



## OpenAPI

````yaml api-specs/ml.json post /quantlib/ml/metrics/classification
openapi: 3.1.0
info:
  title: FinceptQuantLib API - ML
  description: >-
    Machine Learning and Credit Risk module endpoints for FinceptQuantLib API.
    Pro Tier access required (5 credits per request). Covers credit scoring,
    model validation, regression models, clustering, anomaly detection, feature
    engineering, and preprocessing.
  version: 3.0.0
  contact:
    name: Fincept API Support
    url: https://fincept.in
servers:
  - url: https://api.fincept.in
    description: Fincept API Production Server
security:
  - APIKeyHeader: []
tags:
  - name: quantlib-ml
    description: Machine Learning and Credit Risk Analytics
    x-displayName: ML
paths:
  /quantlib/ml/metrics/classification:
    post:
      tags:
        - quantlib-ml
      summary: Classification Evaluation Metrics
      description: >-
        Calculates classification metrics: accuracy, precision, recall, and
        F1-score. Essential for evaluating binary classification models like
        credit default prediction. [Tier: ENTERPRISE, Credits: 10]
      operationId: classification_metrics
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              required:
                - y_true
                - y_pred
              properties:
                y_true:
                  type: array
                  description: True class labels
                  items:
                    type: integer
                    enum:
                      - 0
                      - 1
                  example:
                    - 0
                    - 0
                    - 1
                    - 1
                    - 0
                    - 1
                    - 0
                    - 1
                y_pred:
                  type: array
                  description: Predicted class labels
                  items:
                    type: integer
                    enum:
                      - 0
                      - 1
                  example:
                    - 0
                    - 0
                    - 1
                    - 1
                    - 0
                    - 0
                    - 0
                    - 1
      responses:
        '200':
          description: Classification metrics
          content:
            application/json:
              schema:
                type: object
                properties:
                  success:
                    type: boolean
                    example: true
                  data:
                    type: object
                    properties:
                      accuracy:
                        type: number
                        description: Accuracy (correct predictions / total)
                        example: 0.875
                      precision:
                        type: number
                        description: Precision (true positives / predicted positives)
                        example: 1
                      recall:
                        type: number
                        description: Recall (true positives / actual positives)
                        example: 0.75
                      f1_score:
                        type: number
                        description: F1 Score (harmonic mean of precision and recall)
                        example: 0.857
        '401':
          $ref: '#/components/responses/UnauthorizedError'
        '402':
          $ref: '#/components/responses/InsufficientCreditsError'
        '422':
          $ref: '#/components/responses/ValidationError'
components:
  responses:
    UnauthorizedError:
      description: Authentication information is missing or invalid
      content:
        application/json:
          schema:
            type: object
            properties:
              detail:
                type: string
                example: Invalid API key
    InsufficientCreditsError:
      description: Insufficient credits for this operation
      content:
        application/json:
          schema:
            type: object
            properties:
              detail:
                type: string
                example: Insufficient credits. This endpoint requires 5 credits.
    ValidationError:
      description: Request validation error
      content:
        application/json:
          schema:
            type: object
            properties:
              detail:
                type: array
                items:
                  type: object
                  properties:
                    loc:
                      type: array
                      items:
                        type: string
                    msg:
                      type: string
                    type:
                      type: string
  securitySchemes:
    APIKeyHeader:
      type: apiKey
      in: header
      name: X-API-Key
      description: >-
        API key for authentication. Get your key at
        https://api.fincept.in/auth/register

````