Table 6 Performance of different classification algorithms: AML dataset, inv(16) problem. Mean test set error (standard deviation) over 100 random splits of the original data into a training set (90%) and a test set (10%). Error is defined as average error per class, i.e. corresponding to assuming a prior probability of occurrence of a class of 50%. Classifiers were trained for 10, 20, 50, 100, 250, 500 and 1000 probe sets selected by the variation filter; results shown here are for the number of probe sets resulting in the smallest average test set error over the four methods, indicated between brackets after the classifier name.

From: The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies