scikit-multilearn
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    • User Guide
      • 1. Introduction
      • 2. Advanced usage

User Guide¶

1. Introduction¶

  • 1. Getting started with scikit-multilearn
  • 2. Relevant Concepts in Multi-Label Classification
    • 2.1. Aim
    • 2.2. Single-label vs multi-label classification
    • 2.3. Multi-label classification data
      • 2.3.1. The multi-label data representation
      • 2.3.2. Single-label representations in problem transformation
  • 3. Dataset handling
    • 3.1. scikit-multilearn format
  • 4. scikit-multilearn repository
    • 4.1. ARFF files
  • 5. How to select a classifier
    • 5.1. Intutions
      • 5.1.1. Generalization quality measures
      • 5.1.2. Performance
    • 5.2. Data-driven model selection
      • 5.2.1. Estimating hyper-parameter k for MLkNN
      • 5.2.2. Estimating hyper-parameter k for embedded classifiers

2. Advanced usage¶

  • 1. Exploring Label Relations
    • 1.1. Detecting communities in Label Relations Graph
      • 1.1.1. Building a Label Graph
      • 1.1.2. NetworkX
  • 2. Using iGraph
  • 3. Stochastic Blockmodel from graph-tool
    • 3.1. Using scikit-learn clusterers
    • 3.2. Fixed partition based on expert knowledge
  • 4. Using the MEKA wrapper
    • 4.1. Setting up MEKA
    • 4.2. Using MEKA via scikit-multilearn
    • 4.3. Citing meka
  • 5. Multi-label deep learning with scikit-multilearn
    • 5.1. Keras
      • 5.1.1. Single-class Keras classifier
      • 5.1.2. Multi-class Keras classifier
    • 5.2. Pytorch
      • 5.2.1. Single-class pytorch classifier
      • 5.2.2. Multi-class pytorch classifier
  • 6. Multi-label embedding-based classification
    • 6.1. Label Network Embeddings
    • 6.2. Cost-Sensitive Label Embedding with Multidimensional Scaling
    • 6.3. Scikit-learn based embedders
  • 7. Multi-label data stratification

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Cite US!

If you use scikit-multilearn in your research and publish it, please consider citing us, it will help us get funding for making the library better. The paper is available on arXiv, to cite it try the Bibtex code on the right.


        
        @ARTICLE{2017arXiv170201460S,
          author = {{Szyma{\'n}ski}, P. and {Kajdanowicz}, T.},
          title = "{A scikit-based Python environment for performing multi-label classification}",
          journal = {ArXiv e-prints},
          archivePrefix = "arXiv",
          eprint = {1702.01460},
          primaryClass = "cs.LG",
          keywords = {Computer Science - Learning, Computer Science - Mathematical Software},
          year = 2017,
          month = feb,
        }
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