Skip to main content

Table 5 Results of random forest and extreme gradient boosting classifier

From: Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

Classifiers 107 3 32 1560 9824 36,961 50,260 86,688
Random forest 0.92 0.89 0.91 0.90 0.86 0.81 0.81 0.82
(No scaling) (booster = gbtree, gblinear, dar) 0.88 0.89 0.88 0.92 0.92 0.92 0.92
0.88 0.89 0.91 0.87 0.82 0.83 0.82
0.88 0.89 0.88 0.92 0.92 0.92 0.92
(No scaling) (loss function = hinge, logistic, logitraw) 0.91 0.89 0.92 0.92 0.92 0.92 0.92
0.92 0.89 0.92 0.93 0.92 0.92 0.93
0.91 0.89 0.92 0.91 0.92 0.92 0.93
(Scaling) (booster = gbtree, gblinear, dar) 0.88 0.89 0.88 0.92 0.92 0.92 0.92
0.85 0.89 0.90 0.86 0.78 0.78 0.73
0.88 0.89 0.88 0.92 0.92 0.92 0.92
(Scaling) (loss function = hinge, logistic, logitraw) 0.91 0.89 0.9211 0.93 0.92 0.92 0.92
0.92 0.89 0.92 0.93 0.92 0.92 0.93
0.91 0.89 0.92 0.91 0.92 0.92 0.93
  1. “–” means no results because of too long computation time. The different boosters used for XGBOOST are gbtree, gblinear, and dar. Different loss functions used for XGBOOST are hinge, logistic, and logitraw. The first row represents the number of SNPs used for Eye-color classification. The first column represents the classifier and different boosters used for the model. Scaling and No Scaling means dataset is scaled or not scaled for particular experiment or not