Linganisha mbinu

	Kujifunza kwa Njia ya Kujitolea kwa Kutumia Wanajamaa-K	Mti wa Kujifunza kwa Kazi (Active Learning Decision Tree)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1951–2010	1984–2010
Mwanzilishi	Settles, B. (active learning framework); Fix & Hodges (KNN base)	Settles, B. (active learning framework); Breiman et al. (decision tree base)
Aina	Active learning with KNN base learner	Active learning with decision tree base learner
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	4	5
Muhtasari	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.