পদ্ধতির তুলনা করুন

	নিয়ন্ত্রিত সিদ্ধান্ত বৃক্ষ	সিদ্ধান্ত বৃক্ষ
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1984	1984
প্রবর্তক	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Breiman, Friedman, Olshen & Stone
ধরন	Supervised learning (regularized tree)	Recursive partitioning (if-then rules)
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.
অপর নাম	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
সম্পর্কিত	6	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.