[KB-291] corehence initial and stacking

report_thesis/src/sections/experiments/stacking_ensemble.tex

@@ -76,7 +76,7 @@ \subsubsection{Results}\label{subsec:stacking_ensemble_results}
 We measured this improvement using \gls{rmsep}, which provides the fairest comparison between the baseline and the stacking approach.
 As mentioned, \gls{rmsep} evaluates the model's performance on the test set.
 In Section~\ref{sec:baseline_replica}, we described how the baseline test set was constructed by sorting extreme concentration values into the training set, and then performing a random split.
-As noted in Section~\ref{subsec:validation_testing_procedures}, required a more sophisticated procedure to support the testing and validation strategy in this work.
+As noted in Section~\ref{subsec:validation_testing_procedures}, a more sophisticated procedure is required to support the testing and validation strategy in this work.
 Despite the differences in test set construction, the test sets remained similar in composition\footnote{The analysis of this can be found on our GitHub repository: \url{https://github.com/chhoumann/thesis-chemcam}}, which allowed us to use \gls{rmsep} as a fair comparison metric.
 Table~\ref{tab:stacking_ensemble_vs_moc} compares the \gls{rmsep} values of different oxides for the \gls{moc} (replica) model with three stacking ensemble models: \gls{enet} with $\alpha = 1$, \gls{enet} with $\alpha = 0.1$, and \gls{svr}.
 Overall, the stacking ensemble models tend to produce lower \gls{rmsep} values compared to the \gls{moc} (replica) model.

report_thesis/src/sections/proposed_approach/proposed_approach.tex

@@ -41,7 +41,7 @@ \section{Proposed Approach}\label{sec:proposed_approach}
 These metrics include the \gls{rmse} for accuracy and the sample standard deviation of prediction errors for robustness.
 By evaluating both cross-validation and test set metrics, we ensure a thorough assessment of the models' generalizability and performance on unseen data.
 
-Next, we implemented an experimental framework using the Optuna optimization library~\cite{optuna_2019}.
+Next, we implemented an optimization framework using Optuna as a foundation~\cite{optuna_2019}.
 This framework facilitates automated hyperparameter optimization, allowing us to efficiently explore a vast search space of model and preprocessing configurations.
 The specifics of this framework are discussed in Section~\ref{sec:optimization_framework}.