A Trade-Off Between Depth and Impurity for Pruning Decision Trees

@misc{citeulike:2806421, abstract = {Most pruning methods for decision trees minimize a classification error rate. In uncertain domains, some sub-trees which do not lessen the error rate can be relevant to point out some populations of specific interest or to give a representation of a large data file.}, author = {Fournier, D. and Cr\'emilleux, B. }, citeulike-article-id = {2806421}, keywords = {naive\_bayes}, posted-at = {2008-05-17 07:05:09}, priority = {2}, title = {A Trade-Off Between Depth and Impurity for Pruning Decision Trees}, url = {http://citeseer.ist.psu.edu/539116.html} }

TY - GEN ID - citeulike:2806421 L3 - citeulike-article-id:2806421 N2 - Most pruning methods for decision trees minimize a classification error rate. In uncertain domains, some sub-trees which do not lessen the error rate can be relevant to point out some populations of specific interest or to give a representation of a large data file. TI - A Trade-Off Between Depth and Impurity for Pruning Decision Trees KW - naive_bayes AU - Fournier, D AU - Cr\'emilleux, B UR - http://citeseer.ist.psu.edu/539116.html ER -