Skip to main content
Ctrl+K
mlquantify homepage mlquantify homepage
  • Install
  • User Guide
  • API
  • Getting Started
  • About Us
  • GitHub
  • Install
  • User Guide
  • API
  • Getting Started
  • About Us
  • GitHub

Section Navigation

  • 1. Foundations
  • 2. Aggregative Quantification
    • 2.1. Using Aggregative Quantification Methods
    • 2.3. Counting-Based Quantifiers
    • 2.5. Adjusted Counting
    • 2.6. Likelihood Methods
    • 2.7. Distribution Matching
    • 2.8. Nearest Neighbours
  • 3. Non Aggregative Quantification
    • 3.1. Mixture Models for Non-Aggregative Quantification
  • 4. Meta Quantification
    • 4.1. Ensembles and Adaptation
    • 4.2. Bootstrap in Quantification
    • 4.3. QuaDapt: Drift-Resilient Score Adaptation
  • 5. Model Selection and Evaluation
    • 5.1. Evaluation Protocols
    • 5.2. Hyperparameter Tuning
    • 5.3. Evaluation Metrics
  • 6. Confidence Intervals
  • 7. Building a Quantifier
  • 8. Mlquantify methods
  • 9. Core Components
    • 9.1. Composable Quantifiers
    • 9.2. Loss Functions
    • 9.3. Representations
    • 9.4. Solvers
    • 9.5. Calibration
    • 9.6. Neural Quantifiers
  • User Guide
  • 9. Core Components

9. Core Components#

These pages document the building blocks that power quantifiers and optimization workflows.

  • 9.1. Composable Quantifiers
    • 9.1.1. Key classes
    • 9.1.2. Linear compose example
    • 9.1.3. Likelihood compose example
  • 9.2. Loss Functions
    • 9.2.1. Available losses
    • 9.2.2. Example
  • 9.3. Representations
    • 9.3.1. Core representations
    • 9.3.2. Example
  • 9.4. Solvers
    • 9.4.1. Available solvers
    • 9.4.2. Example
  • 9.5. Calibration
    • 9.5.1. Available classes
    • 9.5.2. Example skeleton
  • 9.6. Neural Quantifiers
    • 9.6.1. QuaNet — Quantification Network

previous

8. MLQuantify Methods

next

9.1. Composable Quantifiers

Show Source