rework of results section

thesis/sections/discussion.tex

@@ -4,6 +4,12 @@
 
 \section{Discussion}\label{sec:discussion}
 
+\paragraph{Limitations.}
+This analysis reports aggregate test-set metrics without uncertainty quantification.
+Because we did not retain per-user predictions, we cannot compute user-level bootstrap confidence intervals or perform paired significance testing.
+As a result, apparent performance differences—especially small ones—may reflect sampling variability.
+Future re-evaluation that stores per-user predictions will enable user-level bootstrapping, calibration assessment, and formal comparisons.
+
 %In this study, we investigated the performance of different machine learning architectures on the task of fertility prediction,
 %with the aim to find a model that performs well for natural family planning and natural contraception on regular and irregular cycles.
 %
@@ -23,6 +29,8 @@
 %Results do not show any clear indication that the temperature contains any patterns useful for the prediction of
 %fertility or the ovulation.
 
+% regarding the different performance increases on historical contexts: It might have been useful to take a closer look
+% at the characteristics / differences for users with many cycles available
 
 % talk about whether bbt / temperature can be used for such a task, discuss bbt doubt papers
 % While previous work has argued against the predictive value of BBT~\cite{some_author_2010}, our findings suggest otherwise.
@@ -77,4 +85,7 @@ Alternatively, a custom architecture could be developed to better reflect the do
 
 Future work may incorporate more advanced hyperparameter optimization techniques,
 such as Bayesian Optimization, Genetic Algorithms, or Neural Architecture Search (NAS),
-to better explore the joint parameter space in a more efficient and principled manner.
+to better explore the joint parameter space in a more efficient and principled manner.
+
+% add improvements in experiments / comparison
+% add better decision base