Table 6.

Comprehensive summary including all models used in this study.

Model	Type/description	Hyperparameters (used in this study)	Evaluation method	Notes/key points
Bayesian Ridge Regression (BRR)	Probabilistic linear regression with Bayesian L2 regularization	n_iter = 300, alpha_1 = 1e-6, alpha_2 = 1e-6, lambda_1 = 1e-6, lambda_2 = 1e-6, tol=1e-3, fit_intercept=True, normalize=False	LOOCV	Suitable for small datasets and correlated features; estimates regularization parameters automatically; provides predictive uncertainty.
Support Vector Regression (SVR)	Nonlinear regression using RBF kernel	kernel=’rbf’, C = 1.0, epsilon = 0.01	LOOCV	Robust to outliers, handles nonlinear relationships; predictions may be combined in ensemble.
Random Forest Regression (RF)	Ensemble of decision trees averaging predictions	n_estimators = 100, max_depth = 3, min_samples_leaf = 2, random_state = 0	LOOCV	Captures nonlinear relationships; shallow trees prevent overfitting small datasets; used in ensemble when ranked top.
Ensemble (Simple Average)	Combination of top 2 models	Equal weighting (0.5 each) of top 2 models based on RMSE	LOOCV predictions averaged	Combines strengths of different models; improves robustness and reduces errors compared to single models.
CNN+LSTM Hybrid Deep Learning	Convolutional + sequential model for capturing spatial and temporal dependencies	Sliding window sequences: window_size = 3, step = 1 Conv1D: filters = 32, kernel_size = 2, activation=’relu’, L2 = 1e-4 LSTM: units = 32, L2 = 1e-4 Dense: units = 16, activation=’relu’, L2 = 1e-4 Dropout: 0.2 Optimizer: Adam loss=MSECallbacks: EarlyStopping (patience = 30), ReduceLROnPlateau (factor = 0.5, patience = 10, min_lr = 1e-6)	5-fold Cross-Validation per material	Captures sequential patterns; combines convolution for spatial correlations and LSTM for temporal dependencies; features and targets scaled per fold; suitable for time-series-like input derived from sliding windows.