Decision Trees in Ecological Modelling

摘要: Decision tree learning is among the most popular machine techniques used for ecological modelling. trees can be to predict value of one or several target (dependent) variables. They are hierarchical structures, where each internal node contains a test on an attribute, branch corresponding outcome test, and leaf giving prediction class variable. Depending whether we dealing with classification (discrete target) regression problem (continuous target), decision called tree, respectively. The common way induce so-called Top-Down Induction Tress (TDIDT). In this chapter, introduce different types trees, present basic algorithms learn them, give overview their applications in include modelling population dynamics habitat suitability organisms (e.g. soil fauna, red deer, brown bears, bark beetles) ecosystems aquatic, arable forest ecosystems) exposed environmental pressures agriculture, forestry, pollution, global warming).