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
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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