sources. The resulting classifiers are indicated by the secondary data source whose gene set was employed to train the classifier. [**A:**]{} The number of single genes was determined during the cross-validation procedure; [**B:**]{} the 50 best scoring single genes were employed.[]{data-label="fig:restricted_genes"}](Plots/Boxplot_Experiment_04_restrictedSG.pdf "fig:"){width="\linewidth"}

B\
![ We compared the performance of the single gene classifier trained on all genes present on the microarray ([*unrestr.*]{}) with the performance of single gene classifiers that only employ genes present in the secondary data sources. The resulting classifiers are indicated by the secondary data source whose gene set was employed to train the classifier. [**A:**]{} The number of single genes was determined during the cross-validation procedure; [**B:**]{} the 50 best scoring single genes were employed.[]{data-label="fig:restricted_genes"}](Plots/Boxplot_Experiment_04_restrictedSG_100_NMCv1.pdf "fig:"){width="\linewidth"}

### Training set size has no significant effect on performance differences {#training-set-size-has-no-significant-effect-on-performance-differences .unnumbered}

A third possible factor that could explain the lack of performance difference between the composite feature classifiers and the single gene classifier is the size of the training set. Recall that in the cross-validation procedure we train on one data set and then test on another data set. We repeat this procedure for all possible pairs of data sets; excluding, of course, training and testing on the 