further work on methodology

thesis/sections/methodology.tex

@@ -501,8 +501,10 @@ Note that convolutional models do not require an explicit resampling parameter,
 (see Section~\ref{subsubsec:temporal_convolution_layer}).
 
 \paragraph{Model Parameter Tuning}
-To identify the most suitable configuration for each model architecture, model-specific hyperparameters were tuned for optimal predictive performance.
-The goal was to balance model complexity and expressiveness in relation to the given input configuration.
+To identify suitable configurations for each model architecture, model-specific hyperparameters were tuned with the goal of optimizing predictive performance.
+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.
 
 \begin{table}[htbp]
     \centering
@@ -555,6 +557,9 @@ joint parameter space in a more efficient and principled manner.
 
 \subsection{Evaluation}\label{subsec:evaluation}
 
+A variety of evaluation metrics have been defined, to best capture each aspect of the performance of an ovulation prediction.
+The evaluation is divided into two subgroups for the two prediction targets, fertility and past-ovulation indicator.
+
 \subsubsection{Evaluation Metrics}\label{subsubsec:evaluation_metrics}
 
 \subsubsection{Baseline Comparisons}\label{subsubsec:baseline_comparisons}