Sammenlign metoder

	Aktiv læring K-nærmeste naboer	Active Learning Decision Tree
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1951–2010	1984–2010
Ophavsperson	Settles, B. (active learning framework); Fix & Hodges (KNN base)	Settles, B. (active learning framework); Breiman et al. (decision tree base)
Type	Active learning with KNN base learner	Active learning with decision tree base learner
Oprindelig kilde	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.
Aliasser	AL-KNN, active KNN, query-based nearest neighbor learning, uncertainty-sampling KNN	AL-DT, active decision tree, query-based decision tree learning, uncertainty-sampling decision tree
Relaterede	4	5
Resumé	Active learning with K-nearest neighbors combines the instance-based prediction of KNN with an iterative query strategy that selects the most informative unlabeled examples for annotation. The model requests labels only for instances where neighborhood vote margins are narrowest, achieving competitive accuracy with far fewer labeled examples than fully supervised KNN on tabular data.	Active learning with a decision tree combines the interpretable structure of a CART-style tree with a query strategy that selects the most informative unlabeled instances for human annotation. The model iteratively requests labels only for examples it is most uncertain about, minimising labeling cost while maximising classification accuracy on tabular data.