Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Active learning Decision tree× | Semi-supervised Decision Tree× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 1984–2010 | 2000s |
| Upphovsperson≠ | Settles, B. (active learning framework); Breiman et al. (decision tree base) | Various (Levin & Shapiro; Zhu & Goldberg lineage) |
| Typ≠ | Active learning with decision tree base learner | Semi-supervised classifier / regressor |
| Ursprungskälla≠ | Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison. link ↗ | Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning. link ↗ |
| Alias | AL-DT, active decision tree, query-based decision tree learning, uncertainty-sampling decision tree | SSDT, semi-supervised tree induction, self-training decision tree, label-propagation tree |
| Närliggande≠ | 5 | 4 |
| Sammanfattning≠ | 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 Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve better accuracy than a fully supervised tree trained on the labeled subset alone, which is especially valuable when labeling is expensive or time-consuming. |
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