পদ্ধতির তুলনা করুন

	নিয়ন্ত্রিত সিদ্ধান্ত বৃক্ষ	সিদ্ধান্ত বৃক্ষ	Extra Trees
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	1984	1984	2006
প্রবর্তক	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Breiman, Friedman, Olshen & Stone	Geurts, P.; Ernst, D.; Wehenkel, L.
ধরন	Supervised learning (regularized tree)	Recursive partitioning (if-then rules)	Ensemble (extremely randomized decision trees)
মৌলিক উৎস	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
অপর নাম	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
সম্পর্কিত	6	5	5
সারসংক্ষেপ	A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees.	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.	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.