## 2 Materials and Methods
### Hyperparameters
Another dimension is the cost of inter-process data transfer if the ensemble approach is deployed. After about fifteen to twenty iterations, the impetus to incorporate further optimization arises. As the approach continues, certain neural networks with multilayer perceptron topologies might saturate available GPU throughput. This phenomenon was identified with MLP expansions that soared above 1.2 million trainable weights, thereby consuming around 45 to 65 s per epoch, given an average dataset scale. Meanwhile, smaller ensembles, even though less flexible, typically demanded around 2 to 5 s for a single training iteration. A \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Hyperparameters** \\ \hline Decision Tree (DT) & ccp\_alpha = 0.0; criterion = gini; min\_impurity\_decrease = 0.0; \\  & min\_samples\_leaf = 1; min\_samples\_split = 2; monotonic\_est = None. \\  & categorical. features = ‘from\_dtype’; early\_stopping = ‘auto’, \\  & l2\_regularization = 0.0; learning\_rate = 0.1; loss = 'log\_loss’; max\_bins = 255; \\ HistGradientBoosting (HGBT) & max\_features = 1.0; max\_iter = 100; max\_leaf\_nodes = 31; \\  & min\_samples\_leaf = 20; n\_iter\_no\_change = 10; scoring = ‘loss’; \\  & validation\_fraction = 0.1. \\ LSTM (CPU) & hidden\_dims {1,247,232; final Linear layer of 645 parameters.} \\ MLP (Neural Net) (CPU) & Linear layers with 1,049,088; 131,328; 32,896; and 645 parameters; \\  & Softmax output \\ MLP (Neural Net) (GPU) & same architecture as CPU; device = cuda : 0. \\ MultominalNB & alpha = 1.0; fit\_prior = True; force\_alpha = True. \\ GaussianNB & var\_smoothing = 1e-09. \\ Regularized Da (RDA) & shrinkage = ‘auto’; solver = ‘Isqr’; covariance\_estimator = None. \\ Linear Da (LDA) & solver = ‘svd’; tol = 0.0001. \\ SVM (linear) & C = 1.0; kernel = ‘linear’; degree = 3; gamma = ‘scale’. \\ SGD-style SVM & alpha = 1.0; fit\_intercept = True; copy\_X = True. \\ CatBoost (CPU) & learning\_rate = 1.0; depth = 2; loss\_function = MultiClass. \\ CatBoost (GPU) & same as CPU task, type = ‘GPU’. \\ LightGBM (CPU) & boosting\_type = ‘gbd’; learning\_rate = 0.1; n\_estimators = 100; \\  & num\_leaves = 31. \\ LightGBM (GPU) & same as CPU; device = ‘GPU’. \\ GRU (CPU) & GRU layer parameters = 935,424; final Linear of 645 parameters. \\ AdaBoost & n\_estimators = 50; learning\_rate = 1.0; algorithm = ‘deprecated’. \\ RNN (vanilla) & RNN layer parameters = 311,808; final Linear of 645 parameters. \\ Stacking Ensemble & base: Random Forest + Linear SVM pipelines; meta-learner: SVM (default). \\ GRAD Boost & loss = 'log\_loss’; learning\_rate = 1.0; n\_estimators = 100; max\_depth = 1. \\ CNN (scratch) & sequential convolutional layers; GPU primary, CPU fallback. \\ ViT-FT & partial fine-tuning; trainable head of 3845 parameters. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter configurations for all evaluated models across methodologies. notion of trade-off emerges: one must weigh the potential for improved accuracy with the resource demands that large-scale models impose. In certain large-scale experiments, such as the CNN-SCRATCH approach, parameter totals soared above 134 million. This strategy risked saturating 12 GB GPU memory, triggering fallback to slower CPU usage or partial mini-batch strategies. Once GPU capacity was exceeded, iterative gradient steps slowed significantly. That slowdown occasionally overshadowed the potential gains of advanced network architectures. Meanwhile, a minimal perceptron with 10k parameters seldom faced memory issues, though it might lack predictive power in high-dimensional tasks. Costs also escalate with data expansions. Additional features might yield better classification boundaries, though they also cause expansions in matrix multiplications, especially in the early input transformations. Feature selection techniques, such as PCA or Relief-based approaches, were introduced partly to keep the dimension stable. That approach can reduce the computational overhead by focusing on only the top relevant features.