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The Random Forest (RF) algorithm [ 8] has become very popular for pattern recognition in omics-scale data, mainly because RF provides two aspects that are very important for data mining: high prediction accuracy and information on variable importance for classification.
The use of both sagittal and oblique coronal MR images is of importance for classification of the position of the disk in that the oblique coronal imaging plane rendered significant complementary information to that of the sagittal images.
This is particularly true for the spectral region between 3.5 to 4.0 ppm where many metabolites resonate, including those of importance for classification (for example carbohydrates, polyols like mannitol and all Hα resonances of amino acid residues).
The other variables showed of lower importance for classification.
In addition to an internal cross-validation RF also calculates estimates of variable importance for classification [ 8].
Meanwhile, numbering and annotating the identified genes are also of great importance for classification and function characterization.
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There is, thus, a need for additional sensitive markers of prognostic and therapeutic importance and for classification.
The method provides an original variable's importance index for classification and regression that can be applied in other fields [ 14] for example, to assess the RS in the reprioritization component of QoL assessments.
For a fixed number of trees, a variable with a larger importance score relative to other variables indicates that the variable is important for classification.
The sampling dates were of minor importance for the classification.
The vast majority of features are bigrams (42 438), unigrams (10 515) and GR triples (11 854), which also explains their importance for the classification.
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