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Six studies [ 71, 73- 77] constructed models based on their findings, including one using ethnographic decision tree modelling[ 71].
Decision tree modelling was used to predict the relative abundance of the functional groups: high fertility response grasses (HFRG), low fertility response grasses (LFRG), legumes and flatweeds.
Decision tree modelling did not contribute insight that was helpful.
Pruning is an important component of traditional decision tree modelling, and is often used in applications of the C4.5 algorithm.
For the purely epistatic XOR model simulations, C4.5 decision tree modelling was performed, as implemented in the J48 algorithm in freely available Weka software [ 37].
A cost-effectiveness analysis of these two diagnostic strategies was undertaken using decision tree modelling to estimate the costs and effects for each strategy.
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Fig. 3 Decision tree model.
A decision tree model with 97.5% classification success was developed based on SO42 − and Cl− variables.
And so their initial product, the thing that they were developing originally was it was a decision tree model.
Weka tool was used, J48 decision tree classifier was applied to construct the decision tree model.
Specifically, we present algorithms for solving decision tree models and influence diagram models of sequential decision problems.
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