Fig. 4

Overcoming spurious correlations: This figures shows how spurious correlations in the M. tuberculosis data affect the models produced by the Set Covering Machine. a For each M. tuberculosis dataset, the proportion of isolates that are identically labeled in each other dataset is shown. This proportion is calculated using Eq. (2). b The antibiotic resistance models learned by the SCM at each iteration of the correlation removal procedure. Each model is represented by a rounded rectangle identified by the round number and the estimated error rate. All the models are disjunctions (logical-OR). The circular nodes correspond to k-mer rules. A single border indicates a presence rule and a double border indicates an absence rule. The numbers in the circles show to the number of equivalent rules. A rule is connected to an antibiotic if it was included in its model. The weight of the edges gives the importance of each rule