AW Moore - Professor School of Computer Science Carnegie …, 2001 - cs.cmu.edu
… • For each record, follow the decisiontree to see what it would predict For what number of records does the decisiontree’s prediction disagree with the true value in the database? …
Y Coadou - EPJ Web of Conferences, 2010 - epj-conferences.org
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CX Ling, Q Yang, J Wang, S Zhang - Proceedings of the twenty-first …, 2004 - dl.acm.org
… In traditional decisiontree algorithms, the majority class is used to label the leaf node. In our case, as the decisiontree is used to make predictions to minimize the total cost, the leaves …
… ” decisiontree during the training phase, and this tree is then used to classify test instances. The tests at the nodes of the constructed tree are … We propose a lazy decisiontree algorithm-…
A Ehrenfeucht, D Haussler - Information and Computation, 1989 - Elsevier
… We define the rank of a decisiontree and show that for any fixed r, the class of all decision trees … We define the rank of a decisiontree and exhibit a learning algorithm that for any target …
… A decisiontree or diagram is a model of the evaluation of a … Decisiontrees find many applications in decision table … Owing to this broad applicability, results about decisiontrees are …
CS Wallace, JD Patrick - Machine Learning, 1993 - Springer
… Since QR presents an important advance in the theory of decisiontree inference, it seems … The DecisionTree problem is an instance of the general regression problem, which may be …
CT Yildiz, E Alpaydin - IEEE Transactions on Neural Networks, 2001 - ieeexplore.ieee.org
… We propose omnivariate trees where the decision node … decisiontree induction method generalizes better than trees with the same types of nodes everywhere and induces small trees…
… Then, we load the R decisiontree library and grow an unconstrained decisiontree. Specifically, we allow the leaf nodes to contain at least one sample, and internal nodes to have at …