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At the other extreme, the neural net model built using a random selection method for the training and test sets would not be expected to be useful, since this model was unable to accurately rank order compounds across the SALI edges.
Every tree in the forest is built using a random subset of samples and variables, hence the name RF.
The PRAiS model was built using a random 70% subset of 10 years of UK national audit data comprising 26 447 surgical episodes.
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The level of divergence was compared to the distribution of divergence levels built using a pseudo-random sample of 1,000,000 amino acid site columns sampled with replacement from the alignment (one site could be sampled more than once).
A sample of random sequences was built using a permutation test.
The random forest classifier [ 36] was built using a two-step approach with the R package randomForest 2.15.0.
We compared drug response signatures built using a penalized linear regression model and two non-linear machine learning techniques, random forest and support vector machine.
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.
A Maximum-Likelihood (ML) tree was built using a heuristic search starting from a neighbour-joining tree and a tree bisection reconnection (TBR) algorithm for branch swapping, with random addition of sequences in PAUP* 4.0 [ 74].
The dataset containing only phenotypic data was analyzed in PAUP* (v.4.0b10) [ 74] using a heuristic search algorithm with starting trees built using random stepwise addition with tree bisection and reconnection (TBR) branch swapping and twenty random addition sequence replicates.
Noteworthy, Koopman et al. [ 17] performs a comparison of several distributional models at clinical IR, including models built using the methods random indexing (RI) [ 48] and latent semantic analysis (LSA) [ 49].
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