Figure 5From: Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stabilityNetwork indicating the synergy between six real datasets. Each node represents a dataset, and each edge the effect on the DLCV error when pooling them. Four different effects were considered, synergy (bright green) when the pooled error is lower than each of the separate errors. Marginal synergy (light blue) when the pooled error is lower than the weighted mean of the separate errors, conversely marginal anti-synergy (yellow) when it is higher. Lastly, true anti-synergy (orange) indicates a higher DLCV error for the pooled dataset.Back to article page