User Guide
- 1. Introduction
- 2. Advanced usage

User Guide¶

1. Introduction¶

1. Getting started with scikit-multilearn
2. Relevant Concepts in Multi-Label Classification
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¶

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,
        }