Salīdzināt metodes

	Aktīvā mācīšanās lēmumu koks	Loģistikās regresijas aktīvā apguve
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1984–2010	1994–2010
Autors	Settles, B. (active learning framework); Breiman et al. (decision tree base)	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)
Tips	Active learning with decision tree base learner	Active learning framework with logistic regression 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-DT, active decision tree, query-based decision tree learning, uncertainty-sampling decision tree	AL-LR, logistic regression active learner, uncertainty sampling logistic regression, pool-based active logistic classifier
Saistītās	5	4
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.	Active Learning with Logistic Regression is an iterative label-efficient framework in which a logistic regression model selects the unlabeled examples it is most uncertain about, an oracle (human annotator) labels them, and the model is retrained — repeating until a labeling budget or accuracy target is met. It dramatically reduces annotation cost compared to random labeling.