Index

_ | A | B | C | D | E | F | G | I | K | L | M | N | O | P | R | S | T | U | W

_

_label_count (skmultilearn.ensemble.RakelD attribute)

A

available_data_sets() (in module skmultilearn.dataset)

B

BinaryRelevance (class in skmultilearn.problem_transform)

C

D

download_dataset() (in module skmultilearn.dataset)

download_meka() (in module skmultilearn.ext)

E

EmbeddingClassifier (class in skmultilearn.embedding)

example_distribution() (in module skmultilearn.model_selection.measures)

F

fit() (skmultilearn.adapt.BRkNNaClassifier method)

G

I

K

L

M

N

O

OpenNetworkEmbedder (class in skmultilearn.embedding)

P

R

S

T

transform() (skmultilearn.cluster.base.GraphBuilderBase method)
- (skmultilearn.cluster.base.LabelCooccurrenceGraphBuilder method)
- (skmultilearn.problem_transform.LabelPowerset method)

U

unique_combinations_ (skmultilearn.problem_transform.LabelPowerset attribute)

W

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,
        }