Table 1 Summary of classification accuracies both for our approach and Random Forests.

From: Discrimination of rosé wines using shotgun metabolomics with a genetic algorithm and MS ion intensity ratios

Method

Number of ratios in the model

Correct classification rate on the learning dataset

2-fold cross-validation average correct classification rate

Corect classification rate on the validation dataset

GA + LDA

2

86.7%

81.7%

86.7%

RF

5112

100%

67.3%

70%

RF

6

100%

86.4%

76.7

RF

2

100%

79.4%

50%