Salīdzināt metodes

	Aktīvā apmācība ar K-tuvākajiem kaimiņiem	Aktīvā mācīšanās lēmumu koks
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1951–2010	1984–2010
Autors	Settles, B. (active learning framework); Fix & Hodges (KNN base)	Settles, B. (active learning framework); Breiman et al. (decision tree base)
Tips	Active learning with KNN base learner	Active learning with decision tree base learner
Pirmavots	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.
Citi nosaukumi	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
Saistītās	4	5
Kopsavilkums	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.