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On the other hand, model trees tend to be more difficult to interpret than regression trees, since a single model tree can have a large number of linear equations (each with tens of descriptors) in its leaves.
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The PART algorithm derives rules from decision trees and decision trees tend to build overly complex models [ 12].
Suburban trees tend to be younger and healthier.
The trees tend to sprout suckers from their roots.
However, decision trees tend to converge to nonglobal optima (global optimization is NP-hard), and by splitting data, tend to block modeling of feature interactions; this defect can be alleviated to some extent through the use of decision forests.
Trees tend to reduce wind speed and improve thermal comfort in winter.
The trees tend to be small; the largest trees are about 5 10 cm in diameter and 6 10 m tall.
The trees tend to be short and scraggly.
Trees tend to be long-lived, some reaching several thousand years old.
Classification trees tend to be unstable in small data sets.
Baysian (BI) trees tend to reflect accepted topologies better than maximum likelihood (ML) and maximum parsimony (MP) trees.
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