> ## 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.

# Logistic Regression for Credit Scoring

> Fits a logistic regression model for binary classification, commonly used for credit default prediction and PD modeling. Returns model coefficients, AIC, BIC, and optional predictions. Use this for developing credit scorecards and probability of default (PD) models. [Tier: ENTERPRISE, Credits: 10]



## OpenAPI

````yaml api-specs/ml.json post /quantlib/ml/credit/logistic-regression
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/credit/logistic-regression:
    post:
      tags:
        - quantlib-ml
      summary: Logistic Regression for Credit Scoring
      description: >-
        Fits a logistic regression model for binary classification, commonly
        used for credit default prediction and PD modeling. Returns model
        coefficients, AIC, BIC, and optional predictions. Use this for
        developing credit scorecards and probability of default (PD) models.
        [Tier: ENTERPRISE, Credits: 10]
      operationId: logistic_regression
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              required:
                - X
                - 'y'
              properties:
                X:
                  type: array
                  description: >-
                    Feature matrix (training data). Each row is a sample, each
                    column is a feature.
                  items:
                    type: array
                    items:
                      type: number
                  example:
                    - - 1.2
                      - 0.5
                      - 3.1
                    - - 2.1
                      - 1.3
                      - 2.5
                    - - 0.8
                      - 0.9
                      - 4.2
                'y':
                  type: array
                  description: Binary target labels (0 = non-default, 1 = default)
                  items:
                    type: integer
                    enum:
                      - 0
                      - 1
                  example:
                    - 0
                    - 1
                    - 0
                predict_X:
                  type: array
                  description: Optional feature matrix for prediction
                  items:
                    type: array
                    items:
                      type: number
                  example:
                    - - 1.5
                      - 0.7
                      - 3.3
      responses:
        '200':
          description: Logistic regression results
          content:
            application/json:
              schema:
                type: object
                properties:
                  success:
                    type: boolean
                    example: true
                  data:
                    type: object
                    properties:
                      coefficients:
                        type: array
                        items:
                          type: number
                        description: Model coefficients for each feature
                        example:
                          - 0.342
                          - -0.156
                          - 0.871
                      intercept:
                        type: number
                        description: Model intercept
                        example: -1.234
                      aic:
                        type: number
                        description: Akaike Information Criterion
                        example: 145.67
                      bic:
                        type: number
                        description: Bayesian Information Criterion
                        example: 152.89
                      predictions:
                        type: array
                        items:
                          type: number
                        description: Predicted probabilities (if predict_X provided)
                        example:
                          - 0.234
        '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

````