## 3 Results
### Performance
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **CPU util \%** & **GPU util \%** & \begin{tabular}{c} FLOPS \\ CPU \\ \end{tabular} & \begin{tabular}{c} FLOPS \\ GPU \\ \end{tabular} & \begin{tabular}{c} **CC CPU** \\ \end{tabular} & \begin{tabular}{c} **CC GPU** \\ \end{tabular} & \
\begin{tabular}{c} **CPU** \\ \end{tabular} &
\begin{tabular}{c} **GPU** \\ \end{tabular} \\ \hline CatBoost & 98.22 & 67.20 & \(1.99\times 10^{15}\) & \(9.60\times 10^{12}\) & \(1.96\times 10^{15}\) & \(6.44\times 10^{12}\) & X & X \\ Gradient Boost & 16.71 & — & \(2.11\times 10^{15}\) & — & \(3.52\times 10^{14}\) & — & X & \\ XGBoost & 23.53 & 82.55 & \(2.10\times 10^{15}\) & \(9.55\times 10^{12}\) & \(4.94\times 10^{14}\) & \(7.89\times 10^{12}\) & X & X \\ LightGBM & 55.46 & 54.75 & \(2.11\times 10^{15}\) & \(2.09\times 10^{15}\) & \(1.17\times 10^{15}\) & \(1.15\times 10^{15}\) & X & X \\ Ada Boost & 16.55 & — & \(2.11\times 10^{15}\) & — & \(3.49\times 10^{14}\) & — & X & \\ HGBT & 56.02 & — & \(2.00\times 10^{15}\) & — & \(1.12\times 10^{15}\) & — & X & \\ Bernoulli NB & 45.93 & — & \(2.06\times 10^{15}\) & — & \(9.48\times 10^{14}\) & — & X & \\ Multinomial NB & 58.90 & — & \(1.91\times 10^{15}\) & — & \(1.12\times 10^{15}\) & — & X & \\ Gaussian NB & 16.94 & — & \(2.08\times 10^{15}\) & — & \(3.53\times 10^{14}\) & — & X & \\ MLP (Neural Net) & 48.86 & 38.82 & \(1.81\times 10^{8}\) & \(9.68\times 10^{12}\) & \(8.84\times 10^{7}\) & \(3.76\times 10^{12}\) & X & X \\ LSTM & 52.27 & 14.46 & \(7.04\times 10^{7}\) & \(6.59\times 10^{7}\) & \(3.68\times 10^{7}\) & \(9.52\times 10^{6}\) & X & X \\ RNN & 49.63 & 14.22 & \(1.83\times 10^{9}\) & \(6.58\times 10^{7}\) & \(9.06\times 10^{8}\) & \(9.36\times 10^{6}\) & X & X \\ GRU & 51.43 & 14.44 & \(1.83\times 10^{9}\) & \(6.58\times 10^{7}\) & \(9.40\times 10^{8}\) & \(9.49\times 10^{6}\) & X & X \\ Bagging & 16.60 & — & \(2.11\times 10^{15}\) & — & \(3.50\times 10^{14}\) & — & X & \\ Stacking & 17.07 & — & \(2.11\times 10^{15}\) & — & \(3.60\times 10^{14}\) & — & X & \\ Random Forest & 16.73 & — & \(2.11\times 10^{15}\) & — & \(3.53\times 10^{14}\) & — & X & \\ RDA & 79.01 & — & \(1.96\times 10^{15}\) & — & \(1.55\times 10^{15}\) & — & X & \\ LDA & 29.52 & — & \(2.01\times 10^{15}\) & — & \(5.94\times 10^{14}\) & — & X & \\ QDA & 57.89 & — & \(2.09\times 10^{15}\) & — & \(1.21\times 10^{15}\) & — & X & \\ DT & 14.22 & — & \(1.85\times 10^{15}\) & — & \(3.20\times 10^{14}\) & — & X & \\ KNN & 17.95 & — & \(1.90\times 10^{15}\) & — & \(3.40\times 10^{14}\) & — & X & \\ C4.5 & 18.30 & — & \(1.88\times 10^{15}\) & — & \(3.25\times 10^{14}\) & — & X & \\ SVM & 60.10 & 12.45 & \(2.05\times 10^{15}\) & \(1.02\times 10^{13}\) & \(1.10\times 10^{15}\) & \(8.50\times 10^{12}\) & X & X \\ CNN scratch VGG-16 & 25.50 & 85.20 & \(1.10\times 10^{8}\) & \(1.20\times 10^{13}\) & \(5.50\times 10^{7}\) & \(4.00\times 10^{12}\) & X & X \\ CNN-VGG-16-TL & 20.30 & 70.45 & \(1.01\times 10^{8}\) & \(9.80\times 10^{12}\) & \(4.80\times 10^{7}\) & \(3.50\times 10^{12}\) & X & X \\ ViT & 30.80 & 77.65 & \(1.25\times 10^{8}\) & \(1.15\times 10^{13}\) & \(6.20\times 10^{7}\) & \(4.20\times 10^{12}\) & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Carbon-equivalent compute for each model and processor. Neural networks show broader fold spread; LSTM variance approximates 0.007, Neural Net CPU variance is 0.009, and CNN-scratch variance is 0.038. Early weight initialisation randomness therefore still modulates mapping, especially within high-capacity kernels. Class-specific confusion mapping yields distinct asymmetry. Spiral galaxies register recall exceeding 0.99 across all top models. Edge-on disks follow closely. Conversely, \"cigar-shaped smooth\" confounds classifiers; recall hovers near 0.96 despite high macro average. Visual inspection illustrates elongated ellipticals occasionally resemble transitional disks; latent feature space entangles them along the major-axis length dimension.