{"type":"ScholarlyArticle","title":"Lookahead and Pathology in Decision Tree Induction","authors":[{"name":"Sreerama K. Murthy","slug":"sreerama-k-murthy","given":"Sreerama K.","family":"Murthy","affiliation":""},{"name":"Steven Salzberg","slug":"steven-salzberg","given":"Steven","family":"Salzberg","affiliation":""}],"abstract":"The standard approach to decision tree induction is a top-down , greedy algorithm that makes locally optimal, irrevocable decisions at each node of a tree. In this paper, we empirically study an alternative approach, in which the algorithms use one-level lookahead to decide what test to use at a node . We systematically compare, using a very large number of real and artificial data sets, th e quality of decision trees induced by the greedy approach to that of trees induced using look ahead. The main observations from our experiments are: (i) the greedy approach consistently produced trees that were just as accurate as trees produced with the much more expensive lookahead step; and (ii) we observed many instances of pathology, i.e. , lookahead produced trees that were both l arger and less accurate than trees produced without it.","doi":null,"url":"https://ijcai.org/proceedings/1995/133","pdf":"https://ijcai.org/proceedings/1995/0133.pdf","markdown":"https://ijcai.org/proceedings/1995/133.md","bibtex":"https://ijcai.org/proceedings/1995/bibtex/133","volume":{"slug":"1995","title":"Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1995","url":"https://ijcai.org/proceedings/1995/","year":1995,"location":"Montréal, Québec, Canada","dates":"","editors":"Chris S. Mellish","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Learning","track":"Learning","number":133,"pages":{"first":1025,"last":1032},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}