User Guide

• 1. Aggregative Quantification
  • 1.1. Counters For Quantification
    • 1.1.1. Classify and Count
    • 1.1.2. Probabilistic Classify and Count
  • 1.2. Adjust Counting
    • 1.2.1. Threshold Adjustment
    • 1.2.2. Matrix Adjustment
  • 1.3. Likelihood-Based Quantification
    • 1.3.1. Maximum Likelihood Prevalence Estimation (MLPE)
    • 1.3.2. Expectation Maximization for Quantification (EMQ)
  • 1.4. Mixture Models
    • 1.4.1. DyS: Distribution y-Similarity Framework
    • 1.4.2. HDy: Hellinger Distance y-Similarity
    • 1.4.3. SMM: Sample Mean Matching
    • 1.4.4. SORD: Sample Ordinal Distance
  • 1.5. Nearest Neighbors
    • 1.5.1. PWK: Pair-wise Weighted K-Nearest Neighbors
  • 1.6. Kernel Density Estimation
    • 1.6.1. KDEy: Kernel Density Estimation y-Similarity
• 2. Non Aggregative Quantification
  • 2.1. Mixture Models for Non-Aggregative Quantification
    • 2.1.1. HDx: Hellinger Distance x-Similarity
• 3. Meta Quantification
  • 3.1. Ensemble for Quantification
  • 3.2. Bootstrap in Quantification
  • 3.3. QuaDapt: Drift-Resilient Score Adaptation
• 4. Model Selection and Evaluation
  • 4.1. Protocols for Quantification
    • 4.1.1. Artificial-Prevalence Protocol (APP)
    • 4.1.2. Natural-Prevalence Protocol (NPP)
    • 4.1.3. Uniform Prevalence Protocol (UPP)
    • 4.1.4. Personalized Prevalence Protocol (PPP)
  • 4.2. Tuning Hyperparameters
  • 4.3. References
  • 4.4. Evaluation Metrics
    • 4.4.1. Single Label Quantification (SLQ) Metrics
    • 4.4.2. Regression-Based Quantification (RQ) Metrics
    • 4.4.3. Ordinal Quantification (OQ) Metrics
• 5. Confidence Intervals
  • 5.1. General Concept
  • 5.2. Percentile-Based Confidence Intervals
  • 5.3. Confidence Ellipse in Simplex
  • 5.4. Confidence Ellipse in CLR Space
    • 5.4.1. References
• 6. Building a Quantifier
  • 6.1. General Quantifiers
  • 6.2. Aggregative Quantifiers
  • 6.3. Aggregation Types
  • 6.4. Binary Quantifiers
  • 6.5. Validation Utilities
  • 6.6. Parameter Constraints