Fig 2. SURFDAWave classifier performance compared to Trendsetter, diploS/HIC, and evolBoosting when differentiating between sweeps and neutrality and trained and tested with simulations based on CEU (top row) and YRI (bottom row) demographic history.

(Left) Power to differentiate between sweep and neutrality by comparing the probability of a sweep under sweep simulations with the same probability in simulations of neutrality including zoomed in region between 0.0 and 0.2 on the x-axis and 0.8 and 1.0 on the y-axis. (Right confusion matrices) Confusion matrices comparing classification rates of the methods. SURFDAWave applied using Daubechies’ least-Asymmetric wavelets to estimate spatial distributions of summary statistics with γ penalties and level chosen through cross validation (see Training the models). Summary statistics $\widehat{\pi }$, H₁, H₁₂, H₂/H₁, and frequency of the first, second, third, fourth, and fifth most common haplotypes used by both Trendsetter and SURFDAWave.