Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Msitu wa Kawaida wa Bahatisha× | Miti ya Ziada× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2012 | 2006 |
| Mwanzilishi≠ | Deng, H. & Runger, G. | Geurts, P.; Ernst, D.; Wehenkel, L. |
| Aina≠ | Regularized ensemble (penalized feature selection in trees) | Ensemble (extremely randomized decision trees) |
| Chanzo asilia≠ | Deng, H., & Runger, G. (2012). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8. DOI ↗ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ |
| Majina mbadala | RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | Regularized Random Forest (RRF), introduced by Deng and Runger in 2012, extends the standard Random Forest by adding a penalty that discourages splits on features not already used in the ensemble. This built-in regularization produces sparser, less redundant feature subsets, making the model especially valuable when feature selection is as important as predictive accuracy. | Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time. |
| ScholarGateSeti ya data ↗ |
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