fig6

Figure 6. Error analysis of SISSO models. (A and B) Residual distributions (left) and residual-predicted value plots (right) for the K_{start_1} model. Training set (1,126 samples): Residuals follow a normal distribution (μ = 4.03 × 10^-5, σ = 0.0252), with 95% within ± 0.05. LOWESS trend line slope = 0.002; Testing set (282 samples): Residuals (μ = -1.61 × 10^-4, σ = 0.0262) show 95% within ± 0.06. LOWESS slope = 0.001; (C and D) Error analysis for the K_{end_2} model. Training set (1,075 samples): Residuals (μ = 1.19 × 10^-4, σ = 3.02 × 10^-3) are 95% within ± 6 × 10^-3. LOWESS slope = 0.009. Testing set (269 samples): Residuals (μ = 9.40 × 10^-5, σ = 2.79 × 10^-3) show 95% within ± 6 × 10^-3. LOWESS slope = 0.002. SISSO: Sure independence screening and sparsity operator; LOWESS: locally weighted scatterplot smoothing.