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Compared to other soft computing methods, model trees are able to offer two main advantages: (a) they are able to provide mathematical equations and offer more insight into the obtained equations and (b) they are more convenient to develop and implement.
However, in the Shell model, trees are given no flexibility in the horizontal placement of their crowns, or the foliage within the crowns.
Regression trees are a formalism for representation of piecewise constant functions, whereas model trees are more general and are a formalism for representation of piecewise linear functions.
For each setting of the parameters, 100 model trees are generated and from each model tree 10 datasets are created; we then average results over the resulting 1,000 trees.
The lack of correlation between the established target variables of personality traits of ex-mercury miners and the indicators of past occupational exposure on the group level, and the small number of persons in certain subgroups constructed in model trees are the main obstacles preventing the more reliable interpretation of results obtained.
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The main advantage of model trees is that, compared to neural networks, they represent understandable rules.
Using categorical and numerical data of those physical, chemical and morphological properties of soils in both tropical and temperate areas, classification trees and model trees were grown.
Results indicate that error statistics of model trees and feed-forward back propagation (FFBP) ANNs were similar, while model trees was marginally more accurate.
The main advantage of model trees is that, unlike the other data learning tools, they are easier to use and more importantly they represent understandable mathematical rules.
We wanted to measure how often the underlying tree(s) from the simulation (from now on, the "model trees") were contained somewhere within the inferred tree(s) or network.
Two sets of model trees were generated: i) 50-taxon trees of height 220 thousand years (tyrs), ii) 100-taxon trees of height 440 tyrs.
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