> ## 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.

# Ensemble Regression (Random Forest, Gradient Boosting)

> Fits ensemble regression models for maximum predictive accuracy. Random Forest for variance reduction and robustness, Gradient Boosting for highest performance. Returns predictions and feature importances. [Tier: ENTERPRISE, Credits: 10]



## OpenAPI

````yaml api-specs/ml.json post /quantlib/ml/regression/ensemble
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/regression/ensemble:
    post:
      tags:
        - quantlib-ml
      summary: Ensemble Regression (Random Forest, Gradient Boosting)
      description: >-
        Fits ensemble regression models for maximum predictive accuracy. Random
        Forest for variance reduction and robustness, Gradient Boosting for
        highest performance. Returns predictions and feature importances. [Tier:
        ENTERPRISE, Credits: 10]
      operationId: ensemble_regression
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              required:
                - X
                - 'y'
              properties:
                X:
                  type: array
                  description: Feature matrix
                  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: Continuous target values
                  items:
                    type: number
                  example:
                    - 150000
                    - 235000
                    - 185000
                method:
                  type: string
                  description: Ensemble method
                  enum:
                    - random_forest
                    - gradient_boosting
                  default: random_forest
                  example: gradient_boosting
                n_estimators:
                  type: integer
                  description: Number of trees
                  default: 100
                  example: 100
                max_depth:
                  type: integer
                  description: Maximum tree depth
                  default: 5
                  example: 5
                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: Ensemble regression results
          content:
            application/json:
              schema:
                type: object
                properties:
                  success:
                    type: boolean
                    example: true
                  data:
                    type: object
                    properties:
                      predictions:
                        type: array
                        items:
                          type: number
                        example:
                          - 197500
        '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

````