手法を比較

	能動学習決定木	決定木
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	1984–2010	1984
提唱者	Settles, B. (active learning framework); Breiman et al. (decision tree base)	Breiman, Friedman, Olshen & Stone
種類	Active learning with decision tree base learner	Recursive partitioning (if-then rules)
原典	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.
別名	AL-DT, active decision tree, query-based decision tree learning, uncertainty-sampling decision tree	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
関連	5	5
概要	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.