Fig 3. Testing of different feature numbers.

The performance of all tested methods, as number of features is increased (x axis). Twenty rounds of 2-fold cross validation are performed using the 1 to 15 features. The performance gradually increases when we add features to the training set. The prediction performance significantly improved when the number of features adopted increases from 1 to 6. No closely related feature was excluded. (A) Cox model. (B) Rank-based Gaussian process regression. (C) Rank-based linear regression. (D) Rank-based random forest.