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For each subset, a classifier is built using the subset and minority samples.
Random forest models were built using the subset of variables selected from the training set, with and without the inclusion of the clinical variables.
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The approach is to build a forest of decision trees, each built using a different subset of the data for training.
Every tree in the forest is built using a random subset of samples and variables, hence the name RF.
In a balanced cohort of 76 patients, a neural network-based prediction model was built using a training subset of the cohort to first identify proteomic patterns of VTE.
In an iterative manner, RF models were then built using feature subsets, starting with the most important and adding one additional feature per round.
Then in i t h validation i=(1,2,…k), the regression function with the parameters (C h, γ o ) is built using k−1 subsets as training set.
The PRAiS model was built using a random 70% subset of 10 years of UK national audit data comprising 26 447 surgical episodes.
Each of the classification trees is built using a bootstrap sample of the data, and at each split the candidate set of variables is comprised of a random subset.
Pedotransfer functions were built using a regression tree model on a subset of the data for which total Si concentration was measured.
The trees are built using binary recursive partitioning which involves the dataset successively being split into increasingly homogeneous subsets according to a pre-specified criterion [ 35], in this case, the presence/absence of disease.
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