QuaPy: A Python-based open-source framework for quantification
  • Quickstart
  • Manuals
    • 1. Datasets
    • 2. Evaluation
    • 3. Explicit Loss Minimization
    • 4. Quantification Methods
    • 5. Model Selection
    • 6. Plotting
    • 7. Protocols
  • API
QuaPy: A Python-based open-source framework for quantification
  • Manuals
  • View page source

Manuals

  • 1. Datasets
    • 1.1. Reviews Datasets
    • 1.2. Twitter Sentiment Datasets
    • 1.3. UCI Machine Learning
    • 1.4. LeQua 2022 Datasets
    • 1.5. IFCB Plankton dataset
    • 1.6. Adding Custom Datasets
  • 2. Evaluation
    • 2.1. Error Measures
    • 2.2. Evaluation Protocols
  • 3. Explicit Loss Minimization
  • 4. Quantification Methods
    • 4.1. Aggregative Methods
    • 4.2. Composable Methods
    • 4.3. Meta Models
  • 5. Model Selection
    • 5.1. Targeting a Quantification-oriented loss
    • 5.2. Targeting a Classification-oriented loss
  • 6. Plotting
    • 6.1. Diagonal Plot
    • 6.2. Quantification bias
    • 6.3. Error by Drift
  • 7. Protocols
    • 7.1. Artificial-Prevalence Protocol
    • 7.2. Sampling from the unit-simplex, the Uniform-Prevalence Protocol (UPP)
    • 7.3. Natural-Prevalence Protocol
    • 7.4. Other protocols
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