{"library":"scikit-multilearn","title":"scikit-multilearn","description":"A BSD-licensed library for multi-label classification built on top of scikit-learn. Current version is 0.2.0. The project appears to be in maintenance mode with no recent releases; last PyPI release was in 2018.","language":"python","status":"maintenance","last_verified":"Mon Apr 27","install":{"commands":["pip install scikit-multilearn"],"cli":null},"imports":["from skmultilearn.problem_transform import BinaryRelevance","from skmultilearn.problem_transform import LabelPowerset","from skmultilearn.problem_transform import ClassifierChain","from skmultilearn.cluster import LabelSpaceClustererBase"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import numpy as np\nfrom skmultilearn.problem_transform import BinaryRelevance\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.datasets import make_multilabel_classification\n\nX, Y = make_multilabel_classification(n_samples=100, n_features=20, n_classes=5, random_state=42)\nclassifier = BinaryRelevance(classifier=RandomForestClassifier(), require_dense=[True, True])\nclassifier.fit(X, Y)\npredictions = classifier.predict(X)\nprint(predictions.shape)","lang":"python","description":"Quick example using BinaryRelevance with a RandomForest base classifier.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}