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Non-parametric test was conducted for a ranked variable such as the frequency of bronchodilator used during asthma attack.
The produced ordered training data with more relevant to less important variables was further processed by meta-node "Build Model by Adding Ranked Variables", which firstly splits data into training and test set and introduces each ranked variable into a new SVMs fitted models (Additional file 1: Figure S5).
Relative indices of inequality are then computed by regressing the mortality on this ranked variable.
For the ranked variable (leaf colour), differences between groups were confirmed in all cases using non-parametric testing (SAS npar1way).
Nested RF modeling was done using the ranked indices, by starting with the highest ranked variable and subsequent addition remaining after each step.
After the initial ordering of the candidate predictors using bootstrapping, our final predictive model is constructed using a stepwise procedure where the top ranked variable from our ordered list enters the model first.
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The Spearman correlation coefficient is defined as the Pearson correlation coefficient between the ranked variables.
Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases.
These ranked variables are sequentially added to the learning algorithm to find the most relevant set of predictors leading to the model of smallest error rates.
To evaluate models predictability, data is split into training and test set to generate and validate stepwise estimation model by sequentially feeding ranked variables.
Round II allowed the participants to rank the organized findings of Round I. Round III provided an opportunity to rescore the ranked variables after viewing other participant's results.
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