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@ -73,7 +73,9 @@ modelizer <- function(dfm, cores_outer, cores_grid, cores_inner, cores_feats, se
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mc.cores = cores_inner
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)
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)
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print(res)
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print(res[1,1])
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print('inner_cv')
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return(cbind(as.data.frame(t(colMeans(select(res, 1:`Balanced Accuracy`)))),params))
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}
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@ -96,6 +98,9 @@ modelizer <- function(dfm, cores_outer, cores_grid, cores_inner, cores_feats, se
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cores_inner = cores_inner,
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mc.cores = cores_grid)
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)
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print(res)
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print(res[1,1])
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print('outer_cv')
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# Determine optimum hyperparameters within outer fold training set
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optimum <- res[which.max(res[,opt_measure]),] %>%
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select(percentiles: ncol(.))
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@ -117,6 +122,9 @@ modelizer <- function(dfm, cores_outer, cores_grid, cores_inner, cores_feats, se
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cores_inner = cores_inner,
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mc.cores = cores_grid)
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)
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print(res)
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print(res[1,1])
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print('line 126, final model parameter optimization')
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return(res)
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}
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}
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