Salīdzināt metodes

	Aktīvā mācīšanās lēmumu koks	Koku lēmumu pieņemšana (Decision Tree)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1984–2010	1984
Autors	Settles, B. (active learning framework); Breiman et al. (decision tree base)	Breiman, Friedman, Olshen & Stone
Tips	Active learning with decision tree base learner	Recursive partitioning (if-then rules)
Pirmavots	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Citi nosaukumi	AL-DT, active decision tree, query-based decision tree learning, uncertainty-sampling decision tree	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Saistītās	5	5
Kopsavilkums	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.