What is Decision stream graph?
[[File:Decision stream.gif|alt=Decision stream|thumb|left|Fig. 1. Decision stream: statistic-based merge of nodes from the same/different levels of predictive model.]] [[File:Decision_Stream_vs._Binary_Tree_(32_nodes).pdf|alt=Decision stream|thumb|Fig. 2. Binary decision stream and tree with the same quantity of nodes.]]Decision stream is a directed acyclic graph of decision [[rules]] for classification and [[regression analysis|regression]] tasks (Fig. 1). This decision tree based [[scientific method|method]] <ref>{{cite book|author1=Ignatov, D.Yu.|author2=Ignatov, A.D.|title=2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) |chapter=Decision Stream: Cultivating Deep Decision Trees |journal=IEEE Ictai|pages=905–912|doi=10.1109/ICTAI.2017.00140|date=2017|arxiv=1704.07657|bibcode=2017arXiv170407657I|isbn=978-1-5386-3876-7|s2cid=21864203|chapter-url=https://www.researchgate.net/publication/316471270}}</ref> avoids the problem of data exhaustion in terminal nodes by merging of leaves from the same/different levels of [[prediction|predictive]] [[computer model|model]]. Decision stream provides: – High accuracy due to the precise splitting of data with unpaired two-sample [[statistical hypothesis test|test statistics]]. – Decrease of overfitting due to partition of data only into [[statistics|statistically]] representative groups. – Reduction of complexity on every level of predictive model. – Self-regulated depth of predictive model. == References == {{reflist}} [[Category:Artificial intelligence]]