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Table 2 Parameters for the four models tested

From: Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry

Model Nbins Mtry Ntree Max depth
Random forest 20, 30, 40 (numeric) 5, 10, 15 200 5, 10, 15
  3 (categorical)    
  Nbins Learn rate ntree Max depth Sample rate
Gradient boosting machine 20, 30, 40 (numeric) 0.1, 0.001 250, 700 5, 10 0.7, 0.8, 0.9
3 (categorical)
  Lambda Alpha
Logistic regression and super learner Range exp(−11) to exp(6) 0–1 by 0.025
  1. Nbins number of bins, Mtry number of splits in branches, Ntree total number of trees grown, Max depth maximum depth to grow the trees (for a detailed description of the model parameters and how they are used see Additional file 3)