## 3 Methodology and Materials
### Data Split  
Partitioning the dataset into training, validation, and test sets is essential for developing predictive models. The training set, which constitutes the majority of the data, allows the model to learn and internalize patterns comprehensively. The test set, distinct from the training phase, provides an unbiased evaluation of the model's generalization capabilities. Additionally, validation subsets derived from the training data function as checkpoints for model refinement, helping to prevent overfitting and ensuring the robustness and reliability of the model's performance.  
As illustrated in Figure 3, the data-splitting strategy begins with dividing the entire dataset into an 80-20 ratio. Eightly percent of the data is designated for the training set, which forms the foundation for autoencoder preprocessing. This training subset is processed through the autoencoder to generate a compressed representation of the input data, a crucial step for effective feature extraction.  
The remaining 20% of the dataset is allocated to the test set, serving as the final assessment of the model's performance. This test set is further divided into validation _VR_sequence_ and _Test_TR_sequence_ partitions, allowing for additional refinement and an evaluation of the model's generalization capabilities.  
The _VR_sequence_, representing a subset comprising one-tenth of the training data, is reintroduced into the autoencoder as a validation set. This procedure facilitates model validation and employs early stopping techniques to mitigate overfitting. As the autoencoder iterates through compressing and reconstructing the training data, the _VR_sequence_ provides an unbiased assessment of the model's ability to generalize beyond the training examples. This subset is crucial for calibrating the model, optimizing hyperparameters, and determining the optimal complexity of the network architecture.  
The early stopping mechanism monitors the validation loss--calculated based on the _VR_sequence_ throughout the training iterations. Training is halted if the validation loss does not show improvement or decreases only slightly over a predetermined number of iterations, known as the \"patience\" parameter. This process, commonly referred to as learning rate annealing or decay, allows the model to make finer adjustments as it approaches the minimum of the loss function, leading to more accurate convergence. The _VR_sequence_, which constitutes a portion of the test set, is employed for final validation across various regression models. This phase serves as a critical checkpoint to ensure that  
Figure 3: Data split flow graph.  
the representations learned by the encoding layer remain robust and accurate when applied to data not used in the initial training phase.  
In the regression model validation stage, the features are generated and optimized through the autoencoder are evaluated using several regression techniques. The performance of these regression models reflects the encoded features' efficacy in capturing the original data's essential characteristics. This evaluation helps to determine the quality of the autoencoder's feature extraction process and its ability to facilitate accurate predictions across various modeling approaches.