Work on thesis

docs/thesis/chapters/5_cases/naive-bayes.tex

@@ -21,6 +21,7 @@ SciBERT's version of the MAG dataset has 84 thousand and 22.3 thousand sentences
 \begin{figure}
     \centering
     \includegraphics[width=0.5\linewidth]{figures/mag-distribution.png}
+    \captionsetup{width=.9\linewidth}
     \caption{Class distribution of the MAG  \cite{wang2019review} dataset's 84000 sentences in its \textit{train} split.}
     \label{fig:mag-distribtion}
 \end{figure}
@@ -30,6 +31,7 @@ SSC is much larger: it contains over 80 million abstracts. Having more data cert
 \begin{figure}
     \centering
     \includegraphics[width=0.8\linewidth]{figures/ss-distribution.png}
+    \captionsetup{width=.9\linewidth}
     \caption{Label distribution of the Semantic Scholar dataset \cite{Lo2020S2ORCTS}. Each publication may be assigned at most 3 domains.}
     \label{fig:ss-distribution}
 \end{figure}
@@ -63,6 +65,7 @@ The sentences are tokenised into words and vectorised with TF-IDF (with logarith
 \begin{figure}
     \centering
     \includegraphics[width=0.8\linewidth]{figures/mag-confusion.png}
+    \captionsetup{width=.9\linewidth}
     \caption{Confusion matrix of a Naïve Bayes classifier on the MAG dataset's sentences. The matrix is normalised column-wise. Notice, how most mistakes happen between semantically similar classes, for instance: \textit{politics} -- \textit{sociology} or \textit{business} -- \textit{economics}.}
     \label{fig:mag-confusion}
 \end{figure}
@@ -70,6 +73,7 @@ The sentences are tokenised into words and vectorised with TF-IDF (with logarith
 \begin{figure}
     \centering
     \includegraphics[width=\linewidth]{figures/ss-confusion.png}
+    \captionsetup{width=.9\linewidth}
     \caption{Confusion matrix of a Naïve Bayes classifier on the SSC dataset's sentences. The matrix is normalised column-wise. Notice, how most mistakes happen between semantically similar classes, for instance:  \textit{philosohpy} -- \textit{sociology} or \textit{history} -- \textit{art}.}
     \label{fig:ss-confusion}
 \end{figure}
@@ -100,7 +104,7 @@ According to the body of research on the adoption of best practices, this is whe
 \textbf{How could we implement more best practices?} The most notable missing best practices are the lack of automated deployment, automated regression testing, online monitoring, persisting the traces, graceful error-handling, taking feedback on the results (if possible in the use-case), calculating the online accuracy based on the feedback, and retraining the model if necessary using novel data. These all correspond to best practices.
 \end{displayquote}
 
-\section{Bridging \textbf{the gap} with GreatAI}
+\section{Bridging the gap with GreatAI}
 
 First, let us revisit the library's scope for clarification. As concluded in Section \ref{section:scope}, GreatAI should ease the \textit{transition} step between prototypes and production-ready deployments. However, this leaves open the question of what constitutes to this step? There are cross-cutting concerns, for example, feature extraction is implemented and used in the training phase but it is also deployed alongside the model. The robustness criterion has to be met by this procedure even though its implementation is only in focus in the earlier stages of the project. Since having an untested function deployed into production can have severe repercussions, I conclude, assuring its correctness lies within the scope of GreatAI.
 
@@ -193,6 +197,7 @@ After implementing some features of the library it can be already used for deplo
 \begin{figure}
     \centering
     \includegraphics[width=0.7\linewidth]{figures/dashboard-domains.png}
+    \captionsetup{width=.9\linewidth}
     \caption{Screenshot of the domain prediction integrated into the ScoutinScience platform where it is used as a filtering option.}
     \label{fig:dashboard-domains}
 \end{figure}