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Posterior probabilities for each split were calculated from the posterior density of trees.
Two separate regression RFs with 700 trees and two variables tried at each split were performed using the package randomForest version 4.6-7 [ 60] in the software program R [ 61].
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Firstly, the dataset was divided into three random splits and secondly each split was divided into training, calibration, test and validation sets.
In the MapReduce model, a large dataset is split and each split sent to a node, also known as a mapper, where each split is independently processed.
Each split is based on a single value of predictor from an exhaustive search of all available predictors to maximize the differences between the offspring branches.
The number of randomly selected variables at each split was varied over five values (generally 2 to the number of predictors in the model).
In this case, there are 2 splits; each split is of size 3.
Each split is determined in a data driven way, with lower splits all conditional on the prior splits.
Then, each split was run on the respective subset of data as represented by the preceding RP split criteria.
After collecting ages for all splits from the posterior, a point estimate of the height at each split is used to build the tree.
At each step the variable used for each split is selected from all the explicative variables so as to provide an optimal partition given the previous actions.
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