diff --git a/report_thesis/src/sections/experiments/stacking_ensemble.tex b/report_thesis/src/sections/experiments/stacking_ensemble.tex
index f43f173b..5aa618ac 100644
--- a/report_thesis/src/sections/experiments/stacking_ensemble.tex
+++ b/report_thesis/src/sections/experiments/stacking_ensemble.tex
@@ -84,7 +84,7 @@ \subsubsection{Results}\label{subsec:stacking_ensemble_results}
 
 \begin{table}
 \centering
-\caption{Stacking ensemble results using the \gls{enet} model as the meta-learner with default hyperparameters.}
+\caption{Stacking ensemble results using the \gls{enet} model as the meta-learner with $\alpha = 1$.}
 \begin{tabular}{lcccc}
 \toprule
 Oxide          & \gls{rmsep} & STDDEV & \gls{rmsecv}         & Std. Dev. CV          \\
@@ -185,14 +185,14 @@ \subsubsection{Results}\label{subsec:stacking_ensemble_results}
 \toprule
 Oxide          & \gls{moc} (replica) & \gls{enet} ($\alpha = 1$) & \gls{enet} ($\alpha = 0.1$) & \gls{svr} \\
 \midrule
-\ce{SiO2}      & 5.61          & 3.588              & 3.598                & \textbf{3.473} \\
-\ce{TiO2}      & 0.61          & 0.571              & \textbf{0.319}       & 0.340 \\
-\ce{Al2O3}     & 2.47          & \textbf{1.656}     & 1.658                & 1.729 \\
-\ce{FeO_T}     & 1.82          & 1.794              & 1.841                & \textbf{1.693} \\
-\ce{MgO}       & 1.56          & \textbf{0.711}     & 0.768                & 0.819 \\
-\ce{CaO}       & 2.09          & \textbf{1.636}     & 1.647                & 1.594 \\
-\ce{Na2O}      & 1.33          & 0.470              & 0.442                & \textbf{0.369} \\
-\ce{K2O}       & 1.91          & \textbf{0.476}     & 0.494                & 0.511 \\
+\ce{SiO2}      & 5.61          & 3.59              & 3.60                & \textbf{3.47} \\
+\ce{TiO2}      & 0.61          & 0.57              & \textbf{0.32}       & 0.34 \\
+\ce{Al2O3}     & 2.47          & \textbf{1.66}     & 1.66                & 1.73 \\
+\ce{FeO_T}     & 1.82          & 1.79              & 1.84                & \textbf{1.69} \\
+\ce{MgO}       & 1.56          & \textbf{0.71}     & 0.77                & 0.82 \\
+\ce{CaO}       & 2.09          & \textbf{1.64}     & 1.65                & 1.59 \\
+\ce{Na2O}      & 1.33          & 0.47              & 0.44                & \textbf{0.37} \\
+\ce{K2O}       & 1.91          & \textbf{0.48}     & 0.49                & 0.51 \\
 \bottomrule
 \end{tabular}
 }
@@ -201,15 +201,13 @@ \subsubsection{Results}\label{subsec:stacking_ensemble_results}
 
 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. Notably, \ce{SiO2}, \ce{TiO2}, \ce{Na2O}, and \ce{K2O} show significant improvements across all stacking ensemble models. For instance, the \gls{rmsep} for \ce{SiO2} is reduced from 5.61 (\gls{moc} (replica)) to around 3.588-3.598 (\gls{enet} with $\alpha = 1$) and further to 3.473 (\gls{svr}). Similarly, \ce{TiO2} shows a reduction from 0.61 (\gls{moc} (replica)) to 0.319-0.340 (\gls{enet} with $\alpha = 1$). 
-
-The improvements are consistent across most oxides, with \gls{enet} and \gls{svr} models both outperforming the \gls{moc} (replica) model. This suggests that the ensemble approach, particularly with these meta-learners, enhances prediction accuracy for the oxides tested.
-
-
-
-
-
+Overall, the stacking ensemble models tend to produce lower \gls{rmsep} values compared to the \gls{moc} (replica) model.
+Notably, \ce{SiO2}, \ce{TiO2}, \ce{Na2O}, and \ce{K2O} show large improvements across all stacking ensemble models.
+For instance, the \gls{rmsep} for \ce{SiO2} is reduced from 5.61 (\gls{moc} (replica)) to around 3.59 (\gls{enet} with $\alpha = 1$) and further to 3.47 (\gls{svr}).
+Similarly, \ce{TiO2} shows a reduction from 0.61 (\gls{moc} (replica)) to 0.32 (\gls{enet} with $\alpha = 0.1$).
 
+The improvements are consistent across most oxides, with \gls{enet} and \gls{svr} models both outperforming the \gls{moc} (replica) model.
+This shows that the ensemble approach, particularly with these meta-learners, enhances prediction accuracy for the oxides we tested.
 
 \begin{figure*}
     \centering