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API Reference","Binary Relevance kNN","Multi-label ARAM","Multilabel k Nearest Neighbours","Multilabel k Nearest Neighbours","skmultilearn.base.base module","skmultilearn.base.problem_transformation module","skmultilearn.cluster.base module","skmultilearn.cluster.fixed module","skmultilearn.cluster.graphtool module","skmultilearn.cluster.igraph module","skmultilearn.cluster.matrix module","skmultilearn.cluster.networkx module","skmultilearn.cluster.random module","skmultilearn.dataset module","Multi-label embedding classification","Cost-Sensitive Label Embedding with Multidimensional Scaling","Label Network Embeddings for Multi-label Classification (LNEMLC)","scikit-learn based embeddings","Network-based label space partition ensemble classification","RAkELd: random label space partitioning with Label Powerset","RAkELo: random overlapping label space division with Label Powerset","skmultilearn.ensemble.voting module","skmultilearn.ext.keras module","skmultilearn.ext.meka module","skmultilearn.model_selection.iterative_stratification module","skmultilearn.model_selection.measures module","Binary Relevance","Classifier Chains","Label Powerset","skmultilearn.utils module","The scikit-multilearn Team","scikit-multilearn benchmark","2. Relevant Concepts in Multi-Label Classification","3. Dataset handling","Developer documentation","<no title>","Estimating parameters","1. Exploring Label Relations","License","4. Using the MEKA wrapper","5. How to select a classifier","5. Multi-label deep learning with scikit-multilearn","6. Multi-label embedding-based classification","7. Multi-label data stratification","1. Getting started with scikit-multilearn","User Guide"],titleterms:{"class":[35,42],The:[31,33],Using:[35,38,40],adapt:0,advanc:46,aim:33,algorithm:0,api:0,approach:0,aram:2,arff:34,base:[5,6,7,18,19,35,38,43],benchmark:32,binari:[1,27],blockmodel:38,build:[35,38],builder:35,chain:28,cite:40,classif:[15,17,19,33,35,43],classifi:[0,28,35,41,42],classs:35,cluster:[0,7,8,9,10,11,12,13,35,38],code:35,commun:38,concept:33,cost:[16,43],data:[0,33,41,44],dataset:[14,34],deep:42,depend:39,detect:38,develop:35,divis:21,docker:35,document:35,driven:41,embed:[0,15,16,17,18,41,43],embedd:43,ensembl:[0,19,22,35],estim:[37,41],exampl:35,expert:38,explor:38,ext:[23,24],extern:0,file:34,fix:[8,38],format:34,from:38,gener:41,get:45,gpl:39,graph:[35,38],graphbuild:35,graphtool:9,guid:46,handl:34,how:41,hyper:41,igraph:[10,38],imag:35,introduct:46,intut:41,iterative_stratif:25,kera:[23,42],knn:1,knowledg:38,label:[0,2,15,16,17,19,20,21,29,33,35,38,42,43,44],learn:[18,35,38,42,43],librari:39,licens:39,lnemlc:17,manipul:0,matrix:11,measur:[26,41],meka:[24,40],mlclassifierbas:35,mlknn:41,model:[0,41],model_select:[25,26],modul:[5,6,7,8,9,10,11,12,13,14,22,23,24,25,26,30],multi:[0,2,15,17,33,42,43,44],multidimension:[16,43],multilabel:[3,4],multilearn:[0,31,32,34,40,42,45],nearest:[3,4],neighbour:[3,4],network:[17,19,43],networkx:[12,38],overlap:21,paramet:[37,41],partit:[19,20,38],perform:41,powerset:[20,21,29],problem:[0,33],problem_transform:6,pytorch:42,qualiti:41,rakeld:20,rakelo:21,random:[13,20,21],refer:0,relat:38,releas:[],relev:[1,27,33],repositori:[34,35],represent:33,scale:[16,43],scikit:[0,18,31,32,34,35,38,40,42,43,45],select:[0,35,41],sensit:[16,43],set:40,singl:[33,42],skmultilearn:[5,6,7,8,9,10,11,12,13,14,22,23,24,25,26,30],space:[0,19,20,21,35],start:45,stochast:38,stratif:44,team:31,test:35,tool:[0,38],transform:[0,33],unit:35,usag:46,user:46,util:30,via:40,vote:22,work:35,wrapper:40,write:35}})