further work on results

thesis/sections/methodology.tex

@@ -500,6 +500,9 @@ To identify suitable configurations for each model architecture, model-specific
 The selected value ranges were intentionally broad to explore the trade-off between model complexity and generalization.
 This allowed assessment of whether increased architectural depth and capacity contribute meaningfully to performance,
 or whether simpler models are sufficient for the task.
+Due to resource and time limitations, not all configuration permutations can be tested.
+Thus, the parameters will be tested on the input configuration with the best MSE on the fertile days for each model,
+as this is the metric that represents the use cases and overall intention the best.
 
 \begin{table}[htbp]
     \centering
@@ -539,6 +542,9 @@ Tables~\ref{tab:lstm_model_hyperparameters} and~\ref{tab:transformer_model_hyper
 for the LSTM-based and Transformer-based models, respectively.
 Note that the same settings were used for the hybrid models, as their architecture beyond the convolutional front end is structurally identical.
 
+Based on the results of the model parameter search, a best parameter set will be selected for each model architecture,
+based on the MSE of the fertility-probability target during the fertile days.
+These model configurations will then be used for further evaluations.
 
 \vspace{0.5em}
 We acknowledge that interactions between input and model parameters may influence final model performance,